Large Relative Uncertainties Research Articles

New Interstellar Extinction Maps Based on Gaia and Other Sky Surveys

We present new three-dimensional (3D) interstellar extinctionmaps in the V and Gaia G filterswithin 2 kpc of the Sun, a 3D differential extinction (dust density) map along the line of sight in the samespace, a 3D map of variations in the ratio of the extinctions in the V and Gaia G filters within 800 pcof the Sun, and a 2D map of total Galactic extinction through the entire dust half-layer from the Sun toextragalactic space for Galactic latitudes |b| 13◦. The 3D maps have a transverse resolution from 3.6to 11.6 pc and a radial resolution of 50 pc. The 2D map has an angular resolution of 6.1 arcmin. Wehave produced these maps based on the Gaia DR3 parallaxes and Gaia, Pan-STARRS1, SkyMapper,2MASS, andWISE photometry for ∼ 100 million stars. We have paid special attention to the space within200 pc of the Sun and high Galactic latitudes as regions where the extinction estimates have had a largerelative uncertainty so far. Our maps estimate the extinction within the Galactic dust layer from the Sunto an extended object or through the entire dust half-layer from the Sun to extragalactic space with anaccuracy σ(AV) = 0.06 mag. This gives a high relative accuracy of extinction estimates even at highGalactic latitudes, where, according to our estimates, the median total Galactic extinction through theentire dust half-layer from the Sun to extragalactic objects is AV = 0.12 ± 0.06 mag. We have shown thatthe presented maps are among the best ones in data volume, space size, resolution, accuracy, and otherproperties.

Nuclear data uncertainty propagation applied to the versatile test reactor conceptual design

The Versatile Test Reactor (VTR) currently under development is a 300 MWth sodium-cooled fast reactor (SFR) fueled with ternary metal alloy fuel, which aims to accelerate the testing of advanced nuclear fuels, materials, instrumentation, and sensors in high flux environments that are necessary to license the next generation of advanced reactor concepts. To support the VTR design process, uncertainties associated with the nuclear data has been propagated through the reactor core neutronics calculation to global parameters of interest, such as the core multiplication factor, kinetic parameters, and various reactivity feedback coefficients, following the sensitivity based uncertainty propagation approach. By folding the sensitivity coefficients, separately computed by the generalized perturbation theory code PERSENT and Monte Carlo code Serpent 2, with the variance–covariance matrices from COMMARA-2.0, we obtain the reaction-wise, isotope-wise, and overall uncertainties for each response of interest due to nuclear data uncertainty. With Serpent 2, the statistical error of the uncertainty is obtained by propagating the statistical error of the sensitivity coefficients through the same process using a newly developed uncertainty propagation method. From both codes, the overall top uncertainty contributors are found to be the cross section of Fe-56 elastic scattering, Na-23 elastic scattering, and U-238 inelastic scattering. The large contributions of the Fe-56 elastic scattering cross sections to global parameters are due to its relatively large relative uncertainty of 5–10% in nuclear data and the large volume of Fe-containing reflector assemblies in the fairly compact VTR core design. Both codes agreed well for the overall uncertainty estimates of all responses of interest, except the delayed neutron fraction, prompt neutron generation time, and the coolant density feedback coefficient, where Serpent 2 yielded a much larger value than PERSENT due to the large statistical error of sensitivity coefficients. The calculated uncertainties are also compared to those associated with other SFR cores. Another outcome of this study is a variance–covariance matrix of reactivity coefficients, which can be used in the subsequent uncertainty propagation to the system level to investigate the impact of identified uncertainties on system responses in the safety analysis.

Measurement of the two-photon excitation cross-section of the 6p′[3/2]2 and 6p′[1/2]0 levels of Xe I at the wavelengths 224.3 and 222.6 nm

The two-photon excitation cross-section is a key parameter for the two-photon absorption laser induced fluorescence (TALIF) method, which is commonly used to measure atomic densities in gaseous media, especially for plasma diagnostics. The method consists in recording the fluorescence signal that follows the resonant absorption of two photons of UV light. Calibration often relies on comparing the signal recorded in the studied sample with the fluorescence produced, at a similar wavelength, in a noble gas vapor, the density of which can be easily known. The ratio of the involved cross-sections however plays an essential role for the accuracy of such measurements. Yet the two-photon excitation cross-section of atomic xenon, which is often used as the reference for oxygen density measurements, was measured only once, at the wavelengths of interest. The aim of the present study has been to consolidate the experimental value of that key parameter. The cross-section is found equal to and for the 6p′[3/2]2 and 6p′[1/2]0 levels, respectively. For the 6p′[3/2]2 level this is more than twice smaller than previously admitted. Even though the necessarily large relative uncertainty of a non-linear cross-section attaches a relatively large uncertainty to this factor of one half, the result suggests that atomic densities already measured by Xe-calibrated TALIF may have to be revised to significantly lower values. The experiments performed also provide an opportunity to revisit the validity of the approximations used for quantitative TALIF measurements and the collisional broadening and pressure shift of the two-photon 6p′[1/2]0 line. A new formula has been used to describe the two-photon absorption of a Gaussian beam in a long gas cell, which makes the decrease of the beam intensity a simple analytic expression even in strong absorption regime, based on a polylogarithmic function of the absorption rate variable.

Shades of dark uncertainty and consensus value for the Newtonian constant of gravitation

The Newtonian constant of gravitation, G, stands out in the landscape of the most common fundamental constants owing to its surprisingly large relative uncertainty, which is attributable mostly to the dispersion of the values measured for it by different methods and in different experiments, each of which may have rather small relative uncertainty.This study focuses on a set of measurements of G comprising results published very recently as well as older results, some of which have been corrected since the original publication. This set is inconsistent, in the sense that the dispersion of the measured values is significantly larger than what their reported uncertainties suggest that it should be. Furthermore, there is a loosely defined group of measured values that lie fairly close to a consensus value that may reasonably be derived from all the measurement results, and then there are one or more groups with measured values farther away from the consensus value, some appreciably higher, others lower.This same general pattern is often observed in many other interlaboratory studies and meta-analyses. In the conventional treatments of such data, the mutual inconsistency is addressed by inflating the reported uncertainties, either multiplicatively, or by the addition of ‘random effects’, both reflecting the presence of dark uncertainty. The former approach is often used by CODATA and by the Particle Data Group, and the latter is common in medical meta-analysis and in metrology. However, both achieve consistency ignoring how the measured values are arranged relative to the consensus value, and measured values close to the consensus value often tend to be penalized excessively, by such ‘extra’ uncertainty.We propose a new procedure for consensus building that models the results using latent clusters with different shades of dark uncertainty, which assigns a customized amount of dark uncertainty to each measured value, as a mixture of those shades, and does so taking into account both the placement of the measured values relative to the consensus value, and the reported uncertainties. We demonstrate this procedure by deriving a new estimate for G, as a consensus value m3 kg−1 s−2, with m3 kg−1 s−2.

Stand-alone uncertainty characterization of GLEAM, GLDAS and MOD16 evapotranspiration products using an extended triple collocation approach

An optimal use of the global scale actual evapotranspiration (AET) products for various hydro-meteorological applications requires a systematic characterization of their uncertainties. This study presents the first application of an extended triple collocation (TC) approach to provide mutually uncorrelated absolute and relative error structure among three readily available AET (MOD16, GLEAM, and GLDAS) products on the point and spatial scale within the extent of Asia. The physical evaluation results of GLEAM, GLDAS and MOD16 exhibited reasonable accuracy compared to the in-situ AET with mean Index of Agreement >0.71, 0.59 and 0.58, respectively, thereby yielding Root Mean Square Error between ∼4–13 mm/8 day over nine AsiaFlux sites representing forest, rice paddy, and grassland biomes. Theoretical uncertainty assessment of four AET dataset combinations revealed that an average ∼1.5–5.5 mm/8 day random error was contributed from in-situ AET, thereby reducing the accuracy of other datasets. GLEAM performed consistently better with least absolute and relative uncertainties over forest compared with rice paddy and grassland surfaces where GLDAS had almost similar errors as those obtained from GLEAM, while MOD16 showed high uncertainties over all vegetation conditions. Interestingly, all four datasets had large relative uncertainties (>25%) for low vegetation compared to the errors of tall canopies. A spatially merged product generated from the least uncertainties showed better agreement in order of GLDAS > GLEAM > MOD16 over 47%, 42% and 11% of the study area. Overall, the application of extended TC approach on the quality of three AET products is a step forward to develop the merged near real-time accurate AET dataset by processing of theoretical and systematic uncertainties in the current AET algorithms.

Analysis of Global LAI/FPAR Products from VIIRS and MODIS Sensors for Spatio-Temporal Consistency and Uncertainty from 2012–2016

The operational Moderate Resolution Imaging Spectroradiometer (MODIS) Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation absorbed by vegetation (FPAR) algorithm has been successfully implemented for Visible Infrared Imager Radiometer Suite (VIIRS) observations by optimizing a small set of configurable parameters in Look-Up-Tables (LUTs). Our preliminary evaluation showed reasonable agreement between VIIRS and MODIS LAI/FPAR retrievals. However, there is a need for a more comprehensive investigation to assure continuity of multi-sensor global LAI/FPAR time series, as the preliminary evaluation was spatiotemporally limited. In this study, we use a multi-year (2012–2016) global LAI/FPAR product generated from VIIRS and MODIS to evaluate for spatiotemporal consistency. We also quantify uncertainty of the product by utilizing available ground measurements. For both consistency and uncertainty evaluation, we account for variations in biome type and temporal resolution. Our results indicate that the LAI/FPAR retrievals from VIIRS and MODIS are consistent at different spatial (i.e., global and site) and temporal (i.e., 8-day, seasonal and annual) scales. The estimate of mean discrepancy (−0.006 ± 0.013 for LAI and −0.002 ± 0.002 for FPAR) meets the stability requirement for long-term LAI/FPAR Earth System Data Records (ESDRs) from multi-sensors as suggested by the Global Climate Observing System (GCOS). It is noteworthy that the rate of retrievals from the radiative transfer-based main algorithm is also comparable between two sensors. However, a relatively larger discrepancy over tropical forests was observed due to reflectance saturation and an unexpected interannual variation of main algorithm success was noticed due to instability in input surface reflectances. The uncertainties/relative uncertainties of VIIRS and MODIS LAI (FPAR) products assessed through comparisons to ground measurements are estimated to be 0.60/42.2% (0.10/24.4%) and 0.55/39.3% (0.11/26%), respectively. Note that the validated LAI were only distributed in low domains (~2.5), resulting in large relative uncertainty. Therefore, more ground measurements are needed to achieve a more comprehensive evaluation result of product uncertainty. The results presented here generally imbue confidence in the consistency between VIIRS and MODIS LAI/FPAR products and the feasibility of generating long-term multi-sensor LAI/FPAR ESDRs time series.

Comparative study of radiometric and calorimetric methods for total hemispherical emissivity measurements

Accurate knowledge of infrared emissivity is important in applications such as surface temperature measurements by infrared thermography or thermal balance for building walls. A comparison of total hemispherical emissivity measurement was performed by two laboratories: the Laboratoire National de Metrologie et d’Essais (LNE) and the Centre d’Etudes et de Recherche en Thermique, Environnement et Systemes (CERTES). Both laboratories performed emissivity measurements on four samples, chosen to cover a large range of emissivity values and angular reflectance behaviors. The samples were polished aluminum (highly specular, low emissivity), bulk PVC (slightly specular, high emissivity), sandblasted aluminum (diffuse surface, medium emissivity), and aluminum paint (slightly specular surface, medium emissivity). Results obtained using five measurement techniques were compared. LNE used a calorimetric method for direct total hemispherical emissivity measurement [1], an absolute reflectometric measurement method [2], and a relative reflectometric measurement method. CERTES used two total hemispherical directional reflectometric measurement methods [3, 4]. For indirect techniques by reflectance measurements, the total hemispherical emissivity values were calculated from directional hemispherical reflectance measurement results using spectral integration when required and directional to hemispherical extrapolation. Results were compared, taking into account measurement uncertainties; an added uncertainty was introduced to account for heterogeneity over the surfaces of the samples and between samples. All techniques gave large relative uncertainties for a low emissive and very specular material (polished aluminum), and results were quite scattered. All the indirect techniques by reflectance measurement gave results within ±0.01 for a high emissivity material. A commercial aluminum paint appears to be a good candidate for producing samples with medium level of emissivity (about 0.4) and with good uniformity of emissivity values (within ±0.015).

Systematic influences on the areas of peaks in gamma-ray spectra that have a large statistical uncertainty

A method is presented for calculating the expected number of counts in peaks that have a large relative peak-area uncertainty and appear in measured gamma-ray spectra. The method was applied to calculations of the correction factors for peaks occurring in the spectra of radon daughters. It was shown that the factors used for correcting the calculated peak areas to their expected values decrease with an increasing relative peak-area uncertainty. The accuracy of taking the systematic influence inducing the correction factors into account is given by the dispersion of the correction factors corresponding to specific peaks. It was shown that the highest accuracy is obtained in the peak analyses with the GammaVision and Gamma-W software.

Contamination on AMS Sample Targets by Modern Carbon is Inevitable

AbstractAccelerator mass spectrometry (AMS) measurements of the radiocarbon content in very old samples are often challenging and carry large relative uncertainties due to possible contaminations acquired during the preparation and storage steps. In case of such old samples, the natural surrounding levels of 14C from gases in the atmosphere, which may well be the source of contamination among others, are 2–3 orders of magnitude higher than the samples themselves. Hence, serious efforts are taken during the preparation steps to have the samples pristine until measurements are performed. As samples often have to be temporarily stored until AMS measurements can be performed, storage conditions also become extremely crucial. Here we describe an assessment of this process of contamination in background AMS samples. Samples, both as pressed graphite (on AMS targets) and graphite powder, were stored in various storage conditions (CO2-spiked air) to investigate the extent of contamination. The experiments clearly show that the pressed targets are more vulnerable to contamination than the unpressed graphite. Experiments conducted with enriched CO2-spiked laboratory air also reveal that the contaminating carbon is not only limited to the target surface but also penetrates into the matrix. A combination of measurements on understanding the chemical nature of the graphitization product, combined with long-available knowledge on “adventitious carbon” from the surface science community, brought us to the conclusion that contamination is to a certain extent inevitable. However, it can be minimized, and should be dealt with by sputter-cleaning the samples individually before the actual measurement.

Reliability of the peak-analysis results in gamma-ray spectrometry for high relative peak-area uncertainties

When measurement results with values near the decision threshold are being considered, a relative uncertainty of 60% is expected. Since such measurement results can be reported, the performance of the peak-analysing software for gamma-ray spectra needs to be examined for peaks that have a large relative uncertainty. The investigation was performed on a series of spectra measured with a HPGe detector under identical counting conditions. It was found that under a limit value of the relative peak area uncertainty the peak-analysis results are reliable with respect to both the peak location and the peak area evaluation. At relative uncertainties exceeding this uncertainty, the probability of type-II errors increases and a systematic influence on the peak area occurs, which originates in fluctuations of the continuous background in the vicinity of the peak. For the counting conditions used in this investigation, the limit relative uncertainty is about 35%, and whereas a systematic influence can be taken into account by a correction factor, the frequency of the type-II errors can only be reduced at the expense of increasing the frequency of the type-I errors.

Random Sampling of Correlated Parameters – a Consistent Solution for Unfavourable Conditions

Two methods for random sampling according to a multivariate lognormal distribution – the correlated sampling method and the method of transformation of correlation coefficients – are briefly presented. The methods are mathematically exact and enable consistent sampling of correlated inherently positive parameters with given information on the first two distribution moments. Furthermore, a weighted sampling method to accelerate the convergence of parameters with extremely large relative uncertainties is described. However, the method is efficient only for a limited number of correlated parameters.

The geography of rainfall in Mauritius: Modelling the relationship between annual and monthly rainfall and landscape characteristics on a small volcanic island

The variability of rainfall has considerable implications on model parameter estimation and calibration, which may lead to a large degree of uncertainty in model output from climate change impact assessments, water resources planning, and management, design of hydraulic works and urban development. Small island developing states (SIDS) are more sensitive, and have a lower capacity to adapt to climate change than mainland countries, yet, estimates of future rainfall over SIDS are subject to large relative uncertainties. Ordinary least squares regression analyses are used to model mean annual and monthly rainfall in Mauritius over the period 2000–2011, and derive a physical basis for understanding spatial patterns in rainfall. The final models incorporate latitude, longitude, slope, distance to coast, elevation and their interactions and account for 68% of the variance in mean annual rainfall and 55–72% of variance in mean monthly rainfall across the island. The variables included in the model and the spatial patterns that they bring about are physically consistent with basic rainfall generating processes. We highlight the value of incorporating the modelled estimates into a hydrological model, and discuss the applicability of our modelling framework in terms of cost, computational efficiency and transferability to other mountainous areas, particularly on small island developing states.

WE-D-BRE-07: Variance-Based Sensitivity Analysis to Quantify the Impact of Biological Uncertainties in Particle Therapy

Purpose: In particle therapy, treatment planning and evaluation are frequently based on biological models to estimate the relative biological effectiveness (RBE) or the equivalent dose in 2 Gy fractions (EQD2). In the context of the linear-quadratic model, these quantities depend on biological parameters (α, β) for ions as well as for the reference radiation and on the dose per fraction. The needed biological parameters as well as their dependency on ion species and ion energy typically are subject to large (relative) uncertainties of up to 20–40% or even more. Therefore it is necessary to estimate the resulting uncertainties in e.g. RBE or EQD2 caused by the uncertainties of the relevant input parameters. Methods: We use a variance-based sensitivity analysis (SA) approach, in which uncertainties in input parameters are modeled by random number distributions. The evaluated function is executed 10{sup 4} to 10{sup 6} times, each run with a different set of input parameters, randomly varied according to their assigned distribution. The sensitivity S is a variance-based ranking (from S = 0, no impact, to S = 1, only influential part) of the impact of input uncertainties. The SA approach is implemented for carbon ion treatment plans on 3D patient data,more » providing information about variations (and their origin) in RBE and EQD2. Results: The quantification enables 3D sensitivity maps, showing dependencies of RBE and EQD2 on different input uncertainties. The high number of runs allows displaying the interplay between different input uncertainties. The SA identifies input parameter combinations which result in extreme deviations of the result and the input parameter for which an uncertainty reduction is the most rewarding. Conclusion: The presented variance-based SA provides advantageous properties in terms of visualization and quantification of (biological) uncertainties and their impact. The method is very flexible, model independent, and enables a broad assessment of uncertainties. Supported by DFG grant WI 3745/1-1 and DFG cluster of excellence: Munich-Centre for Advanced Photonics.« less

Two-pion low-energy contribution to the muong−2with improved precision from analyticity and unitarity

The two-pion contribution from low energies to the muon magnetic moment anomaly, although small, has a large relative uncertainty since in this region the experimental data on the cross sections are neither sufficient nor precise enough. It is therefore of interest to see whether the precision can be improved by means of additional theoretical information on the pion electromagnetic form factor, which controls the leading order contribution. In the present paper we address this problem by exploiting analyticity and unitarity of the form factor in a parametrization-free approach that uses the phase in the elastic region, known with high precision from the Fermi-Watson theorem and Roy equations for $\pi\pi$ elastic scattering as input. The formalism also includes experimental measurements on the modulus in the region 0.65-0.70 GeV, taken from the most recent $e^+e^-\to \pi^+\pi^-$ experiments, and recent measurements of the form factor on the spacelike axis. By combining the results obtained with inputs from CMD2, SND, BABAR and KLOE, we make the predictions $a_\mu^{\pi\pi, \LO}\,[2 m_\pi,\, 0.30 \gev]=(0.553 \pm 0.004) \times 10^{-10}$ and $a_\mu^{\pi\pi, \LO}\,[0.30 \gev,\, 0.63 \gev]=(133. 083 \pm 0.837)\times 10^{-10}$. These are consistent with the other recent determinations, and have slightly smaller errors.

Challenges and solutions for random sampling of parameters with extremely large uncertainties and analysis of the 232Th resonance covariances

Covariance data in the existing evaluated nuclear data libraries often include large relative uncertainties and mathematical inconsistencies, which arise especially in combination with random sampling. The 232Th evaluation from the ENDF/B-VII.1 library has been taken as an example. Possible solutions for mathematically impossible correlation matrices with negative eigenvalues and too low correlation coefficients between inherently positive parameters with large relative uncertainties are proposed. Convergence of the random sampling for lognormal distribution with extremely high relative standard deviations is slow by nature. Using weighted sampling, single parameters or a limited number of correlated parameters with large uncertainties can be sampled. Efficient sampling of a large number of correlated parameters with extremely large relative uncertainties remains unsolved.

Uncertainty Models for Estimates of Physical Characteristics of River Segments Over Large Areas

AbstractSpatially comprehensive estimates of the physical characteristics of river segments over large areas are required in many large‐scale analyses of river systems and for the management of multiple basins. Remote sensing and modeling are often used to estimate river characteristics over large areas, but the uncertainties associated with these estimates and their dependence on the physical characteristics of the segments and their catchments are seldom quantified. Using test data with varying degrees of independence, we derived analytical models of the uncertainty associated with estimates of upstream catchment area (CA), segment slope, and mean annual discharge for all river segments of a digital representation of the hydrographic network of France. Although there were strong relationships between our test data and estimates at the scale of France, there were also large relative local uncertainties, which varied with the physical characteristics of the segments and their catchments. Discharge and CA were relatively uncertain where discharge was low and catchments were small. Discharge uncertainty also increased in catchments with large rainfall events and low minimum temperature. The uncertainty of segment slope was strongly related to segment length. Our uncertainty models were consistent across large regions of France, suggesting some degree of generality. Their analytical formulation should facilitate their use in large‐scale ecological studies and simulation models.

Transformation of correlation coefficients between normal and lognormal distribution and implications for nuclear applications

Inherently positive parameters with large relative uncertainties (typically ≳30%) are often considered to be governed by the lognormal distribution. This assumption has the practical benefit of avoiding the possibility of sampling negative values in stochastic applications. Furthermore, it is typically assumed that the correlation coefficients for comparable multivariate normal and lognormal distributions are equivalent. However, this ideal situation is approached only in the linear approximation which happens to be applicable just for small uncertainties. This paper derives and discusses the proper transformation of correlation coefficients between both distributions for the most general case which is applicable for arbitrary uncertainties. It is seen that for lognormal distributions with large relative uncertainties strong anti-correlations (negative correlations) are mathematically forbidden. This is due to the asymmetry that is an inherent feature of these distributions. Some implications of these results for practical nuclear applications are discussed and they are illustrated with examples in this paper. Finally, modifications to the ENDF-6 format used for representing uncertainties in evaluated nuclear data libraries are suggested, as needed to deal with this issue.

On random sampling of correlated resonance parameters with large uncertainties

Three different methods for multivariate random sampling of correlated resonance parameters are proposed: the diagonalization method, the Metropolis method, and the correlated sampling method. For small relative uncertainties (typical for s-wave resonances) and weak correlations all methods are equivalent. Differences arise under difficult conditions: large relative uncertainties of inherently positive parameters (typical for widths of higher-l-wave resonances) and/or strong correlations between a large number of parameters. The methods are tested on realistic examples; advantages and disadvantages of each method are pointed out. The correlated sampling method is the only method which produces consistent samples under any conditions. In the field of reactor physics, these methods are mostly used for the sampling of nuclear data, however, they may be used for any data with given uncertainties and correlations.

Evaluation of gamma-ray spectrometric results near the decision threshold

A computerized procedure for analyzing high-resolution gamma-ray spectra was improved in three regards: the peak areas having large relative uncertainties were corrected for the possible contribution of statistical fluctuations in the continuous background, the peaks having a negative net peak area after background subtraction were included in the activity calculations and the primary measurement results were converted to the best estimate using an application of the Bayes theorem. It was proven empirically that the improvements that were introduced diminish the probability and severity of type-I errors and that they improve the consistency and accuracy of the measurement results near the decision threshold.

Direct determination of the absorbed dose to water from 125I low dose-rate brachytherapy seeds using the new absorbed dose primary standard developed at ENEA-INMRI

Low-intensity radioactive sources emitting low-energy photons are used in the clinic for low dose-rate brachytherapy treatments of tumours. The dosimetry of these sources is based on reference air kerma rate measurements. The absorbed dose rate to water at the reference depth d0 = 1 cm, , is then obtained by a conversion procedure with a large relative standard uncertainty of about 5%. This paper describes a primary standard developed at ENEA-INMRI to directly measure due to LDR sources. The standard is based on a large-angle and variable-volume ionization chamber, embedded in a graphite phantom and operating under ‘wall-less air chamber’ conditions. A set of correction and conversion factors, based on experiments and Monte Carlo simulations, are determined to obtain the value of Dw,1 cm from measurements of increment of ionization current with increasing chamber volume. The relative standard uncertainty on is 2.6%, which is appreciably lower than the current uncertainty. Characteristics of the standard, its associated uncertainty budget, and some experimental results are given for 125I BEBIG I25.S16.C brachytherapy seeds. Finally, results of the experimental determination of the dose-rate constant Λ1 cm, traceable to the Dw,1 cm and the low-energy air kerma ENEA-INMRI standards, are given. The relative standard uncertainty on Λ1 cm is 2.9%, appreciably lower than the typical uncertainty (4.8%) of the values available in the literature.