Known Sources Of Uncertainty Research Articles

Uncertainty of scintillator-based field-output factor measurements in MR-Linacs with the two-channel chromatic stem removal technique

Current international protocols such as the IAEA/AAPM TRS-483 for small field dosimetry do not include recommendations for measurements in strong magnetic fields as found in MR-Linacs (i.e. megavoltage x-ray radiotherapy units with built-in magnetic resonance imaging). A EURAMET research project was therefore launched to support the traceability of such measurements and to contribute to the metrological basis for future recommendations. Given the high degree of water equivalence and the small size of fiber-coupled plastic scintillators, one objective of the project was to study the suitability of this detector type for dosimetry in MR-Linacs and to demonstrate their use in a clinical environment. This manuscript reports results of that work. A protocol for measuring field output factors with plastic scintillation detectors was developed. The protocol is based on calibration of the scintillators directly in a water phantom positioned in the MR-Linac rather than in a water-equivalent solid phantom as has been a common technique for conventional linacs. A detailed uncertainty budget was established involving all known sources of uncertainty including the systematic influence of light attenuation through the fiber when measuring in different field sizes. The protocol was used to measure field output factors at five MR-Linac's (four Viewray MRIdian's and one Elekta Unity). The protocol showed a high level of robustness leading to a less than 0.3 % deviation between field output factors calculated based on calibration data obtained before or after the field-output factor measurements. The correction for light attenuation was less than 0.4 % for all fields measured but showed a systematic offset, with larger corrections needed for small field sizes.This robustness of the protocol was further demonstrated by the closeness of agreement (agreement within uncertainty) for field-output factor measurements with two different scintillators (BCF-12 and BCF-60) for square fields (size: 0.83 cm × 0.83 cm or larger) in four nominally identical Viewray MRIdian's. To test the accuracy of the scintillator measurements, the system was used for characterization of a PTW 60019 micro diamond detector in a Viewray MRIdian. Scintillator based correction factors for output factor measurements with the micro diamond detector were found to be in agreement with independent Monte-Carlo computations. The study therefore supports that plastic scintillation detectors are an excellent option for measuring field output factors in MR-Linacs.

Integrating Hierarchical Statistical Models and Machine-Learning Algorithms for Ground-Truthing Drone Images of the Vegetation: Taxonomy, Abundance and Population Ecological Models

In order to fit population ecological models, e.g., plant competition models, to new drone-aided image data, we need to develop statistical models that may take the new type of measurement uncertainty when applying machine-learning algorithms into account and quantify its importance for statistical inferences and ecological predictions. Here, it is proposed to quantify the uncertainty and bias of image predicted plant taxonomy and abundance in a hierarchical statistical model that is linked to ground-truth data obtained by the pin-point method. It is critical that the error rate in the species identification process is minimized when the image data are fitted to the population ecological models, and several avenues for reaching this objective are discussed. The outlined method to statistically model known sources of uncertainty when applying machine-learning algorithms may be relevant for other applied scientific disciplines.

Evaluation of Satellite-Derived Surface Soil Moisture Products over Agricultural Regions of Canada

Soil moisture is a critical indicator for climate change and agricultural drought, but its measurement is challenging due to large variability with land cover, soil type, time, space and depth. Satellite estimates of soil moisture are highly desirable and have become more widely available over the past decade. This study investigates and compares the performance of four surface soil moisture satellite datasets over Canada, namely, Soil Moisture and Ocean Salinity Level 3 (SMOS L3), versions 3.3 and 4.2 of European Space Agency Climate Change Initiative (ESA CCI) soil moisture product and a recent product called SMOS-INRA-CESBIO (SMOS-IC) that contains corrections designed to reduce several known sources of uncertainty in SMOS L3. These datasets were evaluated against in situ networks located in mostly agricultural regions of Canada for the period 2012 to 2014. Two statistical comparison methods were used, namely, metrics for mean soil moisture and median of metrics. The results suggest that, while both methods show similar comparisons for regional networks, over large networks, the median of metrics method is more representative of the overall correlation and variability and is therefore a more appropriate method for evaluating the performance of satellite products. Overall, the SMOS products have higher daily temporal correlations, but larger biases, against in situ soil moisture than the ESA CCI products, with SMOS-IC having higher correlations and smaller variability than SMOS L3. The SMOS products capture daily wetting and drying events better than the ESA CCI products, with the SMOS products capturing at least 75% of observed drying as compared to 55% for the ESA CCI products. Overall, for periods during which there are sufficient observations, both SMOS products are more suitable for agricultural applications over Canada than the ESA CCI products, even though SMOS-IC is able to capture soil moisture variability more accurately than SMOS L3.

Hierarchical Bayesian Thermonuclear Rate for the 7Be(n,p)7Li Big Bang Nucleosynthesis Reaction

Abstract Big Bang nucleosynthesis provides the earliest probe of standard model physics, at a time when the universe was less than 1000 seconds old. It determines the abundances of the lightest nuclides, which give rise to the subsequent history of the visible matter in the universe. This work derives new 7Be(n,p)7Li thermonuclear reaction rates based on all available experimental information. This reaction sensitively impacts the primordial abundances of 7Be and 7Li during big bang nucleosynthesis. We critically evaluate all available data and disregard experimental results that are questionable. For the nuclear model, we adopt an incoherent sum of single-level, two-channel, R-matrix approximation expressions, which are implemented into a hierarchical Bayesian model, to analyze the remaining six data sets we deem most reliable. In the fitting of the data, we consistently model all known sources of uncertainty, including discrepant absolute normalizations of different data sets, and also take the variation of the neutron and proton channel radii into account, hence providing less biased estimates of the 7Be(n,p)7Li thermonuclear rates. From the resulting posteriors, we extract R-matrix parameters (E r , , ) and derive excitation energies and partial and total widths. Our fit is sensitive to the contributions of the first three levels above the neutron threshold. Reaction rates were computed by integrating 10,000 samples of the reduced cross section. Our 7Be(n,p)7Li thermonuclear rates have uncertainties between 1.5% and 2.0% at temperatures of ≤1 GK. We compare our rates to previous results and find that the 7Be(n,p)7Li rates most commonly used in big bang simulations have uncertainties that are too optimistic.

Precipitation phase uncertainty in cold region conceptual models resulting from meteorological forcing time-step intervals

Abstract Precipitation phase determination is a known source of uncertainty in surface-based hydrological, ecological, safety, and climate models. This is primarily due to the surface precipitation phase being a result of cloud and atmospheric properties not measured at surface meteorological or hydrological stations. Adding to the uncertainty, many conceptual hydrological models use a 24-h average air temperature to determine the precipitation phase. However, meteorological changes to atmospheric properties that control the precipitation phase often substantially change at sub-daily timescales. Model uncertainty (precipitation phase error) using air temperature (AT), dew-point temperature (DP), and wet-bulb temperature (WB) thresholds were compared using averaged and time of observation readings at 1-, 3-, 6-, 12-, and 24-h periods. Precipitation phase uncertainty grew 35–65% from the use of 1–24 h data. Within a sub-dataset of observations occurring between AT −6 and 6 °C representing 57% of annual precipitation, misclassified precipitation was 7.9% 1 h and 11.8% 24 h. Of note, there was also little difference between 1 and 3 h uncertainty, typical time steps for surface meteorological observations.

Implications of Improved Representation of Convection for the East Africa Water Budget Using a Convection-Permitting Model

Abstract The precipitation and diabatic heating resulting from moist convection make it a key component of the atmospheric water budget in the tropics. With convective parameterization being a known source of uncertainty in global models, convection-permitting (CP) models are increasingly being used to improve understanding of regional climate. Here, a new 10-yr CP simulation is used to study the characteristics of rainfall and atmospheric water budget for East Africa and the Lake Victoria basin. The explicit representation of convection leads to a widespread improvement in the intensities and diurnal cycle of rainfall when compared with a parameterized simulation. Differences in large-scale moisture fluxes lead to a shift in the mean rainfall pattern from the Congo to Lake Victoria basin in the CP simulation—highlighting the important connection between local changes in the representation of convection and larger-scale dynamics and rainfall. Stronger lake–land contrasts in buoyancy in the CP model lead to a stronger nocturnal land breeze over Lake Victoria, increasing evaporation and moisture flux convergence (MFC), and likely unrealistically high rainfall. However, for the mountains east of the lake, the CP model produces a diurnal rainfall cycle much more similar to satellite estimates, which is related to differences in the timing of MFC. Results here demonstrate that, while care is needed regarding lake forcings, a CP approach offers a more realistic representation of several rainfall characteristics through a more physically based realization of the atmospheric dynamics around the complex topography of East Africa.

Astrophysical S-factors, Thermonuclear Rates, and Electron Screening Potential for the 3He(d,p)4He Big Bang Reaction via a Hierarchical Bayesian Model

Abstract We developed a hierarchical Bayesian framework to estimate S-factors and thermonuclear rates for the 3He(d,p)4He reaction, which impacts the primordial abundances of 3He and 7Li. The available data are evaluated and all direct measurements are taken into account in our analysis for which we can estimate separate uncertainties for systematic and statistical effects. For the nuclear reaction model, we adopt a single-level, two-channel approximation of R-matrix theory, suitably modified to take the effects of electron screening at lower energies into account. In addition to the usual resonance parameters (resonance location and reduced widths for the incoming and outgoing reaction channel), we include the channel radii and boundary condition parameters in the fitting process. Our new analysis of the 3He(d,p)4He S-factor data results in improved estimates for the thermonuclear rates. This work represents the first nuclear rate evaluation using R-matrix theory embedded into a hierarchical Bayesian framework, properly accounting for all known sources of uncertainty. Therefore, it provides a test bed for future studies of more complex reactions.

Optimization of pulse width for frequency measurement by the method of rational approximations principle

The purpose of any measurement is to obtain the required approximation to the measurand. In particular, when measuring time-frequency parameters, traditional methods require more time for measurement, if the frequency value has to be measured more accurately. It is proved that the principle of rational approximations is a method for estimating an unknown frequency, which is very precise, and requires a relatively short time for measurement. Its main properties are: (i) insensitivity to most known sources of uncertainty; (ii) the time required for the measurement is reduced when measuring higher frequencies; (iii) the accuracy of measurements depends on the stability of the reference frequency standard. The recently investigated property of the principle of rational approximations is the dependence of the measurement error on the width of the pulse in the signals used for measurement. In particular, if the pulse width is smaller, the error in the measurement process decreases more rapidly. However, a generally recognized model of analysing how short the pulse width should be is not yet created. In the present paper, a formalism has been developed to determine the optimum pulse width in order to minimize both the magnitude of the error and the measurement time. Variation of this parameter permits to obtain the best approximation to the measurand from the very first moments of measurement. The minimum time required for the measurement was thoroughly analysed.

Derivation of CYP3A4 and CYP2B6 degradation rate constants in primary human hepatocytes: A siRNA-silencing-based approach

The first-order degradation rate constant (kdeg) of cytochrome P450 (CYP) enzymes is a known source of uncertainty in the prediction of time-dependent drug–drug interactions (DDIs) in physiologically-based pharmacokinetic (PBPK) modelling. This study aimed to measure CYP kdeg using siRNA to suppress CYP expression in primary human hepatocytes followed by incubation over a time-course and tracking of protein expression and activity to observe degradation. The magnitude of gene knockdown was determined by qPCR and activity was measured by probe substrate metabolite formation and CYP2B6-Glo™ assay. Protein disappearance was determined by Western blotting. During a time-course of 96 and 60 h of incubation, over 60% and 76% mRNA knockdown was observed for CYP3A4 and CYP2B6, respectively. The kdeg of CYP3A4 and CYP2B6 protein was 0.0138 h−1 (±0.0023) and 0.0375 h−1 (±0.025), respectively. The kdeg derived from probe substrate metabolism activity was 0.0171 h−1 (±0.0025) for CYP3A4 and 0.0258 h−1 (±0.0093) for CYP2B6. The CYP3A4 kdeg values derived from protein disappearance and metabolic activity were in relatively good agreement with each other and similar to published values. This novel approach can now be used for other less well-characterised CYPs.

Contributions of different bias‐correction methods and reference meteorological forcing data sets to uncertainty in projected temperature and precipitation extremes

AbstractThe use of different bias‐correction methods and global retrospective meteorological forcing data sets as the reference climatology in the bias correction of general circulation model (GCM) daily data is a known source of uncertainty in projected climate extremes and their impacts. Despite their importance, limited attention has been given to these uncertainty sources. We compare 27 projected temperature and precipitation indices over 22 regions of the world (including the global land area) in the near (2021–2060) and distant future (2061–2100), calculated using four Representative Concentration Pathways (RCPs), five GCMs, two bias‐correction methods, and three reference forcing data sets. To widen the variety of forcing data sets, we developed a new forcing data set, S14FD, and incorporated it into this study. The results show that S14FD is more accurate than other forcing data sets in representing the observed temperature and precipitation extremes in recent decades (1961–2000 and 1979–2008). The use of different bias‐correction methods and forcing data sets contributes more to the total uncertainty in the projected precipitation index values in both the near and distant future than the use of different GCMs and RCPs. However, GCM appears to be the most dominant uncertainty source for projected temperature index values in the near future, and RCP is the most dominant source in the distant future. Our findings encourage climate risk assessments, especially those related to precipitation extremes, to employ multiple bias‐correction methods and forcing data sets in addition to using different GCMs and RCPs.

Testing feldspar luminescence dating of young archaeological heated materials using potshards from Pella (Tell Tabqat Fahl) in the Jordan valley

Recent developments in the use of more stable feldspar signals in the luminescence dating of sediments offer the possibility of obtaining accurate feldspar luminescence ages for ceramic artefacts; this is especially interesting in locations which do not provide suitable quartz extracts. Here we examine the application of the stable infrared stimulated luminescence signal measured at elevated temperature (in this case 290°C; pIRIR<sub>290</sub>) after stimulation at about room temperature to Levantine pottery samples. A total of 52 potsherds were collected from three superimposed iron-age units at Pella (Jordan); based on <sup>14</sup>C dating, typology and seriation these units were deposited between 700 and 900 BCE. Sand-sized quartz extracts were unsuitable, and there was insufficient sand-sized feldspar, and so polymineral fine grains were chosen for dating. Various tests for reliability were undertaken (dose recovery, dependence of D<sub>e</sub> on first stimulation temperature <i>etc</i>.). The pIRIR signals are weak, and 14 potsherds were rejected on this basis. Of the remainder, 3 were confidently identified as outliers. Based on those sherds for which IR signals were sufficiently intense, we use the ratio of the IR<sub>50</sub> to pIRIR<sub>290</sub> signals to argue that these outliers do not arise from incomplete resetting during manufacture. The ages from each layer are considerably over dispersed (typically by ∼25%) but average ages for each unit are consistent with each other and with the expected age range. The average OSL age for the site is 2840 ± 220 years (n = 35), with the overall uncertainty dominated by systematic uncertainties; this average is consistent with the range of <sup>14</sup>C ages from 970–1270 BCE reported from across the destruction horizon. We conclude that the pIRIR<sub>290</sub> signal is delivering accurate ages, but that the variability in age from shard to shard is much greater than would be expected from known sources of uncertainty. This demonstrates the need for site ages to be based on multiple samples; individual shard ages are unlikely to be sufficiently accurate.

A virtual clinical trial comparing static versus dynamic PET imaging in measuring response to breast cancer therapy

We developed a method to evaluate variations in the PET imaging process in order to characterize the relative ability of static and dynamic metrics to measure breast cancer response to therapy in a clinical trial setting. We performed a virtual clinical trial by generating 540 independent and identically distributed PET imaging study realizations for each of 22 original dynamic fluorodeoxyglucose (18F-FDG) breast cancer patient studies pre- and post-therapy. Each noise realization accounted for known sources of uncertainty in the imaging process, such as biological variability and SUV uptake time. Four definitions of SUV were analyzed, which were SUVmax, SUVmean, SUVpeak, and SUV50%. We performed a ROC analysis on the resulting SUV and kinetic parameter uncertainty distributions to assess the impact of the variability on the measurement capabilities of each metric. The kinetic macro parameter, Ki, showed more variability than SUV (mean CV Ki = 17%, SUV = 13%), but Ki pre- and post-therapy distributions also showed increased separation compared to the SUV pre- and post-therapy distributions (mean normalized difference Ki = 0.54, SUV = 0.27). For the patients who did not show perfect separation between the pre- and post-therapy parameter uncertainty distributions (ROC AUC < 1), dynamic imaging outperformed SUV in distinguishing metabolic change in response to therapy, ranging from 12 to 14 of 16 patients over all SUV definitions and uptake time scenarios (p < 0.05). For the patient cohort in this study, which is comprised of non-high-grade ER+ tumors, Ki outperformed SUV in an ROC analysis of the parameter uncertainty distributions pre- and post-therapy. This methodology can be applied to different scenarios with the ability to inform the design of clinical trials using PET imaging.

Does dispersal capacity matter for freshwater biodiversity under climate change?

Summary Freshwater ecosystems appear to be sensitive to even minor climatic shifts, and the dendritic nature of rivers as well as patchy distribution of habitats within the terrestrial landscape could limit the ability of species to track suitable climate conditions. Although the importance of dispersal is recognised in theory, there is great uncertainty when quantifying the capacity of species to shift their distributions in response to climate change. The influence of dispersal capacity on species’ vulnerability to climate change was assessed, using the modelled projections of 527 freshwater species in New South Wales (NSW), Australia. Species’ future ranges were calculated by iteratively identifying colonisation of accessible habitats and loss of suitable habitats within network models. The accessibility of new habitats was based on a given dispersal mode (aquatic, semi‐terrestrial and aerial). The relative impact of dispersal parameters on projected range were evaluated alongside other known sources of uncertainty (climate and emissions scenarios, modelling algorithm and biological group), analysed collectively in a generalised additive mixed‐model, and spatially to locate regions of NSW where projections are associated with the most uncertainty. Our simulations (1.4 million scenario combinations) suggest at least a third of species will lose more than half their range under climate change. Nevertheless, we emphasise the broad uncertainty that any average encapsulates. Dispersal capacity only had a minor impact on projected range shifts relative to other modelling assumptions but the network‐pathways and maps of uncertainty have value for conservation planning at large scales. Projected range losses initially decreased rapidly as dispersal rates increased but the benefits are reduced above 2–3 km year−1. Taxa restricted to dispersal within the stream network (aquatic) were more vulnerable to climate change than taxa with semi‐terrestrial or aerial dispersal and maps of variation due to dispersal mode and rate indicate where habitat connectivity would be most beneficial. This study demonstrates the breadth of uncertainties that challenge plans for improving ecosystem adaptation under climate change and highlights where in the landscape those differences were consistent. We emphasise the need for freshwater conservation studies to be ecologically representative, to focus on broad‐scale connectivity for taxa that can move between catchments, and an accessible network of refugia for taxa with more limited dispersal.

Importance of tree basic density in biomass estimation and associated uncertainties: a case of three mangrove species in Tanzania

Aboveground and belowground tree basic densities varied between and within the three mangrove species. If appropriately determined and applied, basic density may be useful in estimation of tree biomass. Predictive accuracy of the common (i.e. multi-species) models including aboveground/belowground basic density was better than for common models developed without either basic density. However, species-specific models developed without basic density performed better than common models including basic density. Reducing Emissions from Deforestation and forest degradation and the role of sustainable forest management, conservation and enhancement of carbon stocks (REDD+) initiatives offer an opportunity for sustainable management of forests including mangroves. In carbon accounting for REDD+, it is required that carbon estimates prepared for monitoring reporting and verification schemes should ensure that all known sources of uncertainty are minimised as much as possible. However, uncertainties of applying indirect method of biomass determination are poorly understood. This study aimed to assess importance of tree basic density in modelling aboveground and belowground biomass and examine uncertainties in estimation of tree biomass using indirect methods. This study focused on three dominant mangrove species (Avicennia marina (Forssk.) Vierh, Sonneratia alba J. Smith and Rhizophora mucronata Lam.) in Tanzania. A total of 120 trees were destructively sampled for aboveground biomass, and 30 among them were sampled for belowground biomass. Tree merchantable volume and both aboveground and belowground basic densities were determined. Biomass models including basic density as a predictor variable were developed using the non-linear mixed-effects modelling approach. Results showed that both tree aboveground and belowground basic density varied significantly between sites between tree species, among individuals of the same species and between tree components. The use of tree- and component-specific aboveground basic density resulted in unbiased tree aboveground biomass estimates; however, uncertainties were high when using aboveground basic density values from the Global Wood Density (GWD) database. Predictive accuracy of the common models including aboveground/belowground basic density was better than for the common models developed previously without basic density. However, the species-specific models developed previously without basic density were superior to the common models including basic density developed in the present study. Tree aboveground and belowground basic densities were useful in modelling tree aboveground and belowground biomass, respectively. This is demonstrated by improved goodness of fit associated with inclusion of basic density. However, species-specific models developed without basic density performed better than common models including basic density. If appropriately determined and applied, basic density may be useful in estimation of tree biomass and hence contribute to improved accuracy of carbon stock estimates for REDD+ and sustainable management of mangroves in general.

A stochastic surplus production model in continuous time

AbstractSurplus production modelling has a long history as a method for managing data‐limited fish stocks. Recent advancements have cast surplus production models as state‐space models that separate random variability of stock dynamics from error in observed indices of biomass. We present a stochastic surplus production model in continuous time (SPiCT), which in addition to stock dynamics also models the dynamics of the fisheries. This enables error in the catch process to be reflected in the uncertainty of estimated model parameters and management quantities. Benefits of the continuous‐time state‐space model formulation include the ability to provide estimates of exploitable biomass and fishing mortality at any point in time from data sampled at arbitrary and possibly irregular intervals. We show in a simulation that the ability to analyse subannual data can increase the effective sample size and improve estimation of reference points relative to discrete‐time analysis of aggregated annual data. Finally, subannual data from five North Sea stocks are analysed with particular focus on using residual analysis to diagnose model insufficiencies and identify necessary model extensions such as robust estimation and incorporation of seasonality. We argue that including all known sources of uncertainty, propagation of that uncertainty to reference points and checking of model assumptions using residuals are critical prerequisites to rigorous fish stock management based on surplus production models.

Integrating environmental and self-report data to refine cannabis prevalence estimates in a major urban area of Switzerland

BackgroundCannabis consumption is a topical subject because of discussions about reviewing current regulations. In this context, having a more comprehensive approach to assess and monitor prevalence and consumption is highly relevant. The objective of this work was to refine current estimates about prevalence of cannabis use by combining self-report data and results derived from wastewater analysis. MethodsSelf-report data was retrieved from surveys conducted in Switzerland and Europe. Wastewater samples were collected at the wastewater treatment plant of Lausanne, western Switzerland, over a 15 months period. The occurrence of 11-nor-9-carboxy-delta-9-tetrahydrocannabinol (THC-COOH), a specific metabolite of delta-9-tetrahydrocannabinol (THC), was monitored. Bayesian hierarchical models were used to estimate consumption, prevalence and number of cannabis users in the investigated area. ResultsAccording to survey data, 12-months prevalence in western Switzerland was estimated to 6.2% of the population aged 15 or older, with an estimated daily cannabis consumption of 8.1gday−1·1000inhab−1 (at 11.2% purity). The integrative model comprising self-report and wastewater data substantially reduced the uncertainty in the estimates and suggested a last-year prevalence of 9.4%, with a daily cannabis consumption of 14.0gday−1·1000inhab−1. ConclusionAlthough in the same order of magnitude, consumption and prevalence estimates obtained with the integrative model were 78% and 52% higher compared to self-report figures, respectively. Interestingly, these figures are similar to discrepancies observed when comparing self-reported alcohol consumption and sales or tax data. The suggested integrative model allowed to account for known sources of uncertainty and provided refined estimates of cannabis prevalence in a major urban area of Switzerland.

Identification and interpretation of sources of uncertainty in soils change in a global systems-based modelling process

In the past, uncertainty analysis in soil research was often reduced to consideration of statistical variation in numerical data relating to model parameters, model inputs or field measurements. The simplified conceptual approach used by modellers in calibration studies can be misleading, because it relates mainly to error minimisation in regression analysis and is reductionist in nature. In this study, a large number of added uncertainties are identified in a more comprehensive attention to the problem. Uncertainties in soil analysis include errors in geometry, position and polygon attributes. The impacts of multiple error sources are described, including covariate error, model error and laboratory analytical error. In particular, the distinction is made between statistical variability (aleatory uncertainty) and lack of information (epistemic uncertainty). Examples of experimental uncertainty analysis are provided and discussed, including reference to error disaggregation and geostatistics, and a systems-based analytic framework is proposed. It is concluded that a more comprehensive and global approach to uncertainty analysis is needed, especially in the context of developing a future soils modelling process for incorporation of all known sources of uncertainty.

Validating predictions of unobserved quantities

The ultimate purpose of most computational models is to make predictions, commonly in support of some decision-making process (e.g., for design or operation of some system). The quantities that need to be predicted (the quantities of interest or QoIs) are generally not experimentally observable before the prediction, since otherwise no prediction would be needed. Assessing the validity of such extrapolative predictions, which is critical to informed decision-making, is challenging. In classical approaches to validation, model outputs for observed quantities are compared to observations to determine if they are consistent. By itself, this consistency only ensures that the model can predict the observed quantities under the conditions of the observations. This limitation dramatically reduces the utility of the validation effort for decision making because it implies nothing about predictions of unobserved QoIs or for scenarios outside of the range of observations. However, there is no agreement in the scientific community today regarding best practices for validation of extrapolative predictions made using computational models. The purpose of this paper is to propose and explore a validation and predictive assessment process that supports extrapolative predictions for models with known sources of error. The process includes stochastic modeling, calibration, validation, and predictive assessment phases where representations of known sources of uncertainty and error are built, informed, and tested. The proposed methodology is applied to an illustrative extrapolation problem involving a misspecified nonlinear oscillator.

An optical lattice clock with accuracy and stability at the 10−18 level

Progress in atomic, optical and quantum science has led to rapid improvements in atomic clocks. At the same time, atomic clock research has helped to advance the frontiers of science, affecting both fundamental and applied research. The ability to control quantum states of individual atoms and photons is central to quantum information science and precision measurement, and optical clocks based on single ions have achieved the lowest systematic uncertainty of any frequency standard. Although many-atom lattice clocks have shown advantages in measurement precision over trapped-ion clocks, their accuracy has remained 16 times worse. Here we demonstrate a many-atom system that achieves an accuracy of 6.4 × 10(-18), which is not only better than a single-ion-based clock, but also reduces the required measurement time by two orders of magnitude. By systematically evaluating all known sources of uncertainty, including in situ monitoring of the blackbody radiation environment, we improve the accuracy of optical lattice clocks by a factor of 22. This single clock has simultaneously achieved the best known performance in the key characteristics necessary for consideration as a primary standard-stability and accuracy. More stable and accurate atomic clocks will benefit a wide range of fields, such as the realization and distribution of SI units, the search for time variation of fundamental constants, clock-based geodesy and other precision tests of the fundamental laws of nature. This work also connects to the development of quantum sensors and many-body quantum state engineering (such as spin squeezing) to advance measurement precision beyond the standard quantum limit.

Measurement error in air pollution epidemiology: Guidance for uncertain times.

EnvironmetricsVolume 24, Issue 8 p. 529-530 Discussion Paper Measurement error in air pollution epidemiology: guidance for uncertain times Roger D. Peng, Corresponding Author Roger D. Peng Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, U.S.A.Correspondence to: Roger D. Peng, Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, U.S.A. [email protected]Search for more papers by this author Roger D. Peng, Corresponding Author Roger D. Peng Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, U.S.A.Correspondence to: Roger D. Peng, Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, U.S.A. [email protected]Search for more papers by this author First published: 21 January 2014 https://doi.org/10.1002/env.2242Citations: 1Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article.Citing Literature Volume24, Issue8December 2013Pages 529-530 RelatedInformation