Timing Estimation Research Articles

OAB-057: Temporal-weight estimation of the copy number alterations of of 1384 Multiple Myeloma patients defines an ancestrality index impacting patients survival

<h3>Background</h3> MM is a hematological malignancy always evolving from pre-malignant stages, with progressive increase of genomic complexity. MM is characterized by a large abundance of copy number alterations (CNA); many of them, regarded as "driver", stack up progressively from early tumor stages, causing biological changes that give rise to tumor hallmarks and malignant phenotypes. The combined application of whole genome analysis and mathematical models allows to deeply describe these alterations and to infer their order of acquisition during oncogenesis from their clonality levels, assuming that clonal ones are more ancestral than subclonal. Aims: (1) To define the temporal order of acquisition of CNA, leading to the onset of symptomatic MM and (2) to define a scoring model able to stratify patients (pts) according to the ancestrality of the alterations observed in their genomic landscape. <h3>Methods</h3> Genomic data collected from a total of 1384 newly diagnosed MM pts were included in the study: SNPs array data were collected from 514 pts of our Institution (BO dataset); in 870 pts, WES data were downloaded from CoMMpass study. CN calls and clonality levels were harmonized by an analysis pipeline including ASCAT, GISTIC v2 and custom R scripts. Timing estimates were obtained with BradleyTerry2 package. Survival analysis were performed on R. <h3>Results</h3> A full call-set of CNAs was obtained by harmonizing BO and CoMMpass datasets. The clonality information was first extrapolated from the whole call-set, to define the temporal order of acquisition of non-primary CNAs. CNAs were then accurately ranked, by using the obtained timing estimates, characterized by a quite narrow confidence interval. Of interest, chr 1q gains and chr 13q losses were frequently clonal and ranked as ancestral events, whereas chr 17p losses were late occurring events. By weighting the CNAs carried by any given pts at diagnosis with their relative timing estimate in a combinatorial process, an Ancestrality Index (AI) was defined for each pts (median AI=3.4, IQR=1.7-6.0). The AI was found to be significantly associated with progression free (PFS) and overall survival (OS) (p3.4 (i.e. with a more "ancestral" profile) had a worse outcome as compared to the rest of pts (OS 40% vs 58%, PFS 42% vs 56%, at a median follow up of 92m and 34m, p<0.001).The risk attributed to this "ancestral" category was independent from other high-risk cytogenetic features (i.e. del17p, t(4;14), t(14;20), t(14;20)). <h3>Conclusions</h3> By means of whole genome analysis and dataset harmonizing, the temporal order of acquisition of MM CNAs has been confidently described. A score reflecting the disease ancestrality of MM pts at diagnosis was generated and associated to survival outcomes. Overall, these findings support the evidence that MM pts at diagnosis carrying an excess of ancestral alterations, expected to likely be drivers, are prone to have a dismal prognosis.

Sample, Quantize, and Encode: Timely Estimation Over Noisy Channels

The effects of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">quantization</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">coding</i> on the estimation quality of Gauss-Markov processes are considered, with a special attention to the Ornstein-Uhlenbeck process. Samples are acquired from the process, quantized, and then encoded for transmission using either <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">infinite incremental redundancy</i> (IIR) or <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fixed redundancy</i> (FR) coding schemes. A fixed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">processing</i> time is consumed at the receiver for decoding and sending feedback to the transmitter. Decoded messages are used to construct a minimum mean square error (MMSE) estimate of the process as a function of time. This is shown to be an increasing functional of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">age-of-information</i> (AoI), defined as the time elapsed since the sampling time pertaining to the latest successfully decoded message. Such functional depends on the quantization bits, codewords lengths and receiver processing time. The goal, for each coding scheme, is to optimize sampling times such that the long-term average MMSE is minimized. This is then characterized in the setting of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">general increasing functionals of AoI,</i> not necessarily corresponding to MMSE, which may be of independent interest in other contexts. We first show that the optimal sampling policy for IIR is such that a new sample is generated only if the AoI exceeds a certain <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">threshold,</i> while for FR it is such that a new sample is delivered <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">just-in-time</i> as the receiver finishes processing the previous one. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Enhanced</i> transmissions schemes are then developed in order to exploit the processing times to make new data available at the receiver sooner. For both IIR and FR, it is shown that there exists an optimal number of quantization bits that balances AoI and quantization errors, and hence minimizes the MMSE. It is also shown that for longer receiver processing times, the relatively simpler FR scheme outperforms IIR.

Efficient linear and unconditionally energy stable schemes for the modified phase field crystal equation

In this paper, we construct efficient schemes based on the scalar auxiliary variable (SAV) block-centered finite difference method for the modified phase field crystal (MPFC) equation, which is a sixth-order nonlinear damped wave equation. The schemes are linear, conserve mass and unconditionally dissipate a pseudo energy. We prove rigorously second-order error estimates in both time and space for the phase field variable in discrete norms. We also present some numerical experiments to verify our theoretical results and demonstrate the robustness and accuracy.

A roadmap for sole Cherenkov radiators with SiPMs in TOF-PET

Time of flight positron emission tomography can strongly benefit from a very accurate time estimator given by Cherenkov radiation, which is produced upon a 511 keV positron–electron annihilation gamma interaction in heavy inorganic scintillators. While time resolution in the order of 30 ps full width at half maximum (FWHM) has been reported using MCP-PMTs and black painted Cherenkov radiators, such solutions have several disadvantages, like high cost and low detection efficiency of nowadays available MCP-PMTs. On the other hand, silicon photomultipliers (SiPMs) are not limited by those obstacles and provide high photon detection efficiency with a decent time response. Timing performance of PbF2 crystals of various lengths and surface conditions coupled to SiPMs was evaluated against a reference detector with an optimized test setup using high-frequency readout and novel time walk correction, with special attention on the intrinsic limits for one detected Cherenkov photon only. The average number of detected Cherenkov photons largely depends on the crystal surface state, resulting in a tradeoff between low photon time spread, thus good timing performance, and sensitivity. An intrinsic Cherenkov photon yield of 16.5 ± 3.3 was calculated for 2 × 2 × 3 mm3 sized PbF2 crystals upon 511 keV γ-deposition. After time walk correction based on the slew rate of the signal, assuming two identical detector arms in coincidence, and using all events, a time resolution of 215 ps FWHM (142 ps FWHM) was obtained for 2 × 2 × 20 mm3 (2 × 2 × 3 mm3) sized PbF2 crystals, compared to 261 ps (190 ps) without correction. Selecting on one detected photon only, a single photon coincidence time resolution of 113 ps FWHM for black painted and 166 ps for Teflon wrapped crystals was measured for 3 mm length, compared to 145 ps (black) and 263 ps (Teflon) for 20 mm length.

Visco-acoustic full waveform inversion: From a DG forward solver to a Newton-CG inverse solver

Full waveform inversion (FWI) entails the ill-posed reconstruction of material parameters (such as sound speed and attenuation) from measurements of complete wave fields (full seismograms). In this paper we present a novel framework for FWI in the visco-acoustic regime. The new framework is based on a new elegant derivation of the system of state and adjoint PDEs which are approximated by the discontinuous Galerkin (DG) method. The inverse problem is then solved by the well established regularization scheme CG-REGINN which has not yet been applied in the context of FWI. For the DG discretization we provide a preconditioner for the efficient computation of the time steps by GMRES which yields optimal convergence estimates in space and time and which is confirmed by numerical tests. The inverse solver expresses the required Fréchet derivative and its adjoint in the DG setting. Successful reconstructions in a simplified cross-well setting serve as a proof of concept for our framework and demonstrate the applicability of our new combination of DG method and inverse solver. Some of the inversion experiments use seismograms generated by an independent finite difference time domain forward solver to avoid inverse crime.

Unconditionally optimal convergence of an energy-conserving and linearly implicit scheme for nonlinear wave equations

In this paper, we present and analyze an energy-conserving and linearly implicit scheme for solving the nonlinear wave equations. Optimal error estimates in time and superconvergent error estimates in space are established without certain time-step restrictions. The key is to estimate directly the solution bounds in the H2-norm for both the nonlinear wave equation and the corresponding fully discrete scheme, while the previous investigations rely on the temporal-spatial error splitting approach. Numerical examples are presented to confirm energy-conserving properties, unconditional convergence and optimal error estimates, respectively, of the proposed fully discrete schemes.

An Adaptive Neural Fuzzy Inference System model for freeway travel time estimation based on existing detector facilities

While volume-counting detectors are currently installed in many freeways and have the capabilities of collecting high-resolution traffic information, travel time estimations are usually only based on spot speeds collected by the detectors, which could only produce rough estimates of travel time during peak hours when roadways are congested. In addition to spot speeds, these detectors provide directional volume counts and occupancy, which are useful for a better travel time estimation. This research used data from volume counting detectors and made Adaptive Neural Fuzzy Inference System (ANFIS) to automatically estimate real-time travel times. ANFIS’s ability to learn traffic patterns allows accurate and reliable real-time travel time estimation even with missing or corrupted data. ANFIS also proves to be a powerful tool for estimating future travel times on freeways. For easier use by practitioners and researchers, the method used in this study was implemented into a software package, named FTTE (Freeway Travel Time Estimator). Finally, a case study was conducted using the new approach and the results were compared for both congested and uncongested traffic conditions.

Convergence of the uniaxial PML method for time-domain electromagnetic scattering problems

In this paper, we propose and study the uniaxial perfectly matched layer (PML) method for three-dimensional time-domain electromagnetic scattering problems, which has a great advantage over the spherical one in dealing with problems involving anisotropic scatterers. The truncated uniaxial PML problem is proved to be well-posed and stable, based on the Laplace transform technique and the energy method. Moreover, the L2-norm and L∞-norm error estimates in time are given between the solutions of the original scattering problem and the truncated PML problem, leading to the exponential convergence of the time-domain uniaxial PML method in terms of the thickness and absorbing parameters of the PML layer. The proof depends on the error analysis between the EtM operators for the original scattering problem and the truncated PML problem, which is different from our previous work (Wei et al. [SIAM J. Numer. Anal. 58 (2020) 1918–1940]).

Enhanced Breathing Pattern Detection during Running Using Wearable Sensors.

Breathing pattern (BP) is related to key psychophysiological and performance variables during exercise. Modern wearable sensors and data analysis techniques facilitate BP analysis during running but are lacking crucial validation steps in their deployment. Thus, we sought to evaluate a wearable garment with respiratory inductance plethysmography (RIP) sensors in combination with a custom-built algorithm versus a reference spirometry system to determine its concurrent validity in detecting flow reversals (FR) and BP. Twelve runners completed an incremental running protocol to exhaustion with synchronized spirometry and RIP sensors. An algorithm was developed to filter, segment, and enrich the RIP data for FR and BP estimation. The algorithm successfully identified over 99% of FR with an average time lag of 0.018 s (−0.067,0.104) after the reference system. Breathing rate (BR) estimation had low mean absolute percent error (MAPE = 2.74 [0.00,5.99]), but other BP components had variable accuracy. The proposed system is valid and practically useful for applications of BP assessment in the field, especially when measuring abrupt changes in BR. More studies are needed to improve BP timing estimation and utilize abdominal RIP during running.

Impulsivity and risk-seeking as Bayesian inference under dopaminergic control.

Bayesian models successfully account for several of dopamine (DA)'s effects on contextual calibration in interval timing and reward estimation. In these models, tonic levels of DA control the precision of stimulus encoding, which is weighed against contextual information when making decisions. When DA levels are high, the animal relies more heavily on the (highly precise) stimulus encoding, whereas when DA levels are low, the context affects decisions more strongly. Here, we extend this idea to intertemporal choice and probability discounting tasks. In intertemporal choice tasks, agents must choose between a small reward delivered soon and a large reward delivered later, whereas in probability discounting tasks, agents must choose between a small reward that is always delivered and a large reward that may be omitted with some probability. Beginning with the principle that animals will seek to maximize their reward rates, we show that the Bayesian model predicts a number of curious empirical findings in both tasks. First, the model predicts that higher DA levels should normally promote selection of the larger/later option, which is often taken to imply that DA decreases 'impulsivity,' and promote selection of the large/risky option, often taken to imply that DA increases 'risk-seeking.' However, if the temporal precision is sufficiently decreased, higher DA levels should have the opposite effect-promoting selection of the smaller/sooner option (higher impulsivity) and the small/safe option (lower risk-seeking). Second, high enough levels of DA can result in preference reversals. Third, selectively decreasing the temporal precision, without manipulating DA, should promote selection of the larger/later and large/risky options. Fourth, when a different post-reward delay is associated with each option, animals will not learn the option-delay contingencies, but this learning can be salvaged when the post-reward delays are made more salient. Finally, the Bayesian model predicts correlations among behavioral phenotypes: Animals that are better timers will also appear less impulsive.

Data-driven prediction of decannulation probability and timing in patients with severe acquired brain injury

Background and objectivesFrom a rehabilitation perspective, removal of tracheostomy in patients with severe acquired brain injuries (sABI) is a crucial step. Predictive parameters for a successful decannulation are currently still a focus of the research for sABI patients, especially for those presenting a disorder of consciousness. For this reason, we adopted a data-driven approach predicting decannulation probability and timing using ensemble learning models in patients in intensive rehabilitation units. Methods327 patients, 186 of which were successfully decannulated during their intensive rehabilitative stay, were recruited in a non-concurrent retrospective study. Decannulation probability and timing were predicted using data available within one week from admission at the rehabilitation unit. Two predictive models were trained and cross-validated independently, with the first being an ensemble of a Support Vector Machine and Random Forests and the second an Adaptive Boosting with a Support Vector Regression as weak learner. Confusion matrix, accuracy and AUC were considered as evaluation metrics for the classifier and median absolute error was considered for the regressor. To quantify the advantages in the clinical practice of using the latter prediction, we compared timing estimation with a timing guess (median) calculated on available data. The comparison was based on a Wilcoxon signed rank test. ResultsDecannulation probability was successfully predicted with an accuracy of 84.8% (AUC = 0.85) and timing with a median absolute error of 25.7 days [IQR = 25.6]. This resulted in a significant improvement with respect to the weaning time guess (p<0.05) with an effect size of 71.7%. Furthermore, dichotomizing the regression prediction with a threshold (3 months from the event), resulted in a prediction accuracy of 77.5% (AUC = 0.82) on the test set. DiscussionsA model capable of providing a prediction on decannulation probability and timing was developed and cross-validated, built on data taken at admission to the intensive rehabilitation unit. Translated in clinical practice, this information can support the clinical decision process and provide a mean to improve both in-hospital and domiciliary care organization.

Efficient unconditionally stable numerical schemes for a modified phase field crystal model with a strong nonlinear vacancy potential

AbstractWe consider numerical approximations for a modified phase field crystal model with a strong nonlinear vacancy potential. Based on the invariant energy quadratization approach and stabilized strategies, we develop linear, unconditionally energy stable numerical schemes using the first‐order Euler method, the second‐order backward differentiation formulas and the second‐order Crank–Nicolson method, respectively. We rigorously prove the unconditional energy stability, the mass conservation of these three numerical schemes and carry out error estimates in time for the first‐order numerical scheme. Various numerical experiments in 2D and 3D are carried out to validate the accuracy, energy stability, mass conservation, and efficiency of the proposed schemes.

Errors and uncertainties associated with the use of unconventional activity data for estimating CO2 emissions: the case for traffic emissions in Japan

CO2 emissions from fossil fuel combustion (FFCO2) are conventionally estimated from fuel used (as activity data (AD)) and CO2 emissions factor. Recent traffic emission changes under the impact of the COVID-19 pandemic have been estimated using emerging non-fuel consumption data, such as human mobility data that tech companies reported as AD, due to the unavailability of timely fuel statistics. The use of such unconventional activity data (UAD) might allow us to provide emission estimates in near-real time; however, the errors and uncertainties associated with such estimates are expected to be larger than those of common FFCO2 inventory estimates, and thus should be provided along with a thorough evaluation/validation of the methodology and the resulting estimates. Here, we show the impact of COVID-19 on traffic CO2 emissions over the first six months of 2020 in Japan. We calculated CO2 monthly emissions using fuel consumption data and assessed the emission changes relative to 2019. Regardless of Japan’s soft approach to COVID-19, traffic emissions significantly declined by 23.8% during the state of emergency in Japan (April–May). We also compared relative emission changes among different estimates available. Our analysis suggests that UAD-based emission estimates during April and May could be biased by −19.6% to 12.6%. We also used traffic count data for examining the performance of UAD as a proxy for traffic and/or CO2 emissions. We found the assumed proportional relationship between traffic changes and CO2 emissions was not enough for estimating emissions with accuracy, and moreover, the traffic-based approach failed to capture emission seasonality. Our study highlighted the challenges and difficulties in repurposing data, especially ones with limited traceability/reproducibility, for modeling human activities and assessing the impact on the environment, and the importance of a thorough error and uncertainty assessment before using these data in policy applications.

Consistent Evidence on Duration Dependence of Price Changes

We develop an estimator and tests of a discrete time mixed proportional hazard (MPH) model of duration with unobserved heterogeneity. We allow for competing risks, observable characteristics, and censoring, and we use linear GMM, making estimation and inference straightforward. With repeated spell data, our estimator is consistent and robust to the unknown shape of the frailty distribution. We apply our estimator to the duration of price spells in weekly store data from IRI. We find substantial unobserved heterogeneity, accounting for a large fraction of the decrease in the Kaplan-Meier hazard with elapsed duration. Still, we show that the estimated baseline hazard rate is decreasing and a homogeneous firm model can accurately capture the response of the economy to a monetary policy shock even if there is significant strategic complementarity in pricing. Using competing risks and spell-specific observable characteristics, we separately estimate the model for regular and temporary price changes and find that the MPH structure describes regular price changes better than temporary ones. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

Sequential estimation of quantiles from delayed observations

The problem of sequentially estimating quantiles is considered in the case when the observations become available at random times. Certain class of sequential estimation procedures which are composed of optimal stopping time and sequential minimum risk invariant estimator of a median is obtained under a invariant loss function and with the observation cost determined by a convex function of the moment of stopping and the number of observations up to this moment.

PCN202 Use of Restricted Mean Survival Time in the Presence of Non-Proportional Hazards As an Alternative Measure of Treatment Benefit in Cost-Effectiveness Analyses

The restricted mean survival time (rmst) is a well-established measure of treatment benefit, that is defined as the average event-free survival time up to a pre-specified time point. The rmst difference (rmstD) between two treatments corresponds to the area between their respective Kaplan Meier curves. This empirical study aimed to explore how to use the rmstD in cost-effectiveness models (CEM). The case study was based on a network of three studies related to lung cancer. Two analyses were conducted: the first considered the pooled Kaplan-Meier estimates (time horizon t* of 25 months) and the second was based on extrapolated mean survival time estimators (t* of 15 years). Distributions minimizing the Akaike Information Criteria were considered for the extrapolations of both the Progression-Free survival (Log-Logistic) and the Overall survival (Weibull), separately. The CEM was designed to allow for the use of the rmstD resulting from a random effect network meta-analysis (NMA). Discounted and undiscounted results were generated. The NMA estimates were highly sensitive to t*. The use of restricted or extrapolated estimates, with or without applying the discount rate led to similar conclusions in the cost-effectiveness assessments. The extrapolated mean survival time estimates allowed to capture all treatment benefit although it relied on a larger set of assumptions. This study demonstrates that the rmstD can be used to develop CEM in oncology, which provides a relevant method in a context of non-proportional hazards. The method is sensitive to the selection of the time horizon. Alternative computational methods of the rmstD need to be further explored to better understand the implications on the results. In practice, extrapolations should be based on real world evidence and expert opinions to better reflect expected long-term outcomes.

Validating timing of salmon smolt runs obtained by telemetry studies

AbstractTelemetry tags are increasingly used in management to monitor the migration timing of Atlantic salmon smolts (Salmo salar L.). It has been postulated that (1) effects from tagging and handling may alter migration behaviour, and (2) that the selection of fish during sampling is not representative of all migrating smolts, yielding bias in migration timing estimates. In the River Dale (Vestland, Norway), five groups of wild Atlantic salmon smolts (N = 385) were tagged in April–May and recaptured in a wolf trap. Migration timing was then compared to the untagged population. Migration timing differed between tagged (12 mm PIT) and untagged fish for 4/5 groups. Only fish tagged at the first time point did not have significantly different timing of migration from untagged counterparts. The relationship between length and the timing of migration was different for initial length and length at recapture; initial length suggested earlier migration of longer fish, but the extra time spent in the river prior to migrating for smolts that were initially smaller compensated for the size difference. The tagging protocol is crucial to obtaining representative migration timing results. Smolts should be tagged as early as possible and include the entire size distribution.

Energy fault detection for small buildings based on peer comparison of estimated operating status

As the penetration of smart meters grows, new energy conservation services using electricity consumption data have drawn much attention. Yet service frameworks for small buildings to provide alerts automatically when a failure in energy conservation is detected have not been sufficiently investigated. Thus, we propose a framework for detecting energy faults based on peer comparison of operating time estimated by self-organizing maps from smart meter data with a coarse time resolution. The framework first defines a common operating status for the business type using the results of operating time estimation from multiple buildings of the same type, and then monitors the operating status at 1-h intervals using only a fault threshold parameter specified by the user. If each estimated operating status is judged to be significantly larger than the average level for the building type, the system identifies it as an energy fault. The results of case studies using an open building energy management system dataset suggest that the proposed framework can detect failures in energy conservation caused by inappropriate HVAC management, which usually results in higher contract power consumption, or by failure to turn off appliances that unnecessarily consume energy after business hours.

Prompt Estimation of Die and Mold Machining Time by AI Without NC Program

Machining time estimation is essential for the due-date estimation of products as well as for production planning. Conventionally, machining time has been estimated by a computer aided manufacturing (CAM) system, which requires time and effort to create its numerical control (NC) program and requires machining expertise to operate it. In addition, among the problems with conventional methods, an error in the estimated machining time arises owing to the machine tool’s control characteristics. In this study, an artificial intelligence (AI)-based system capable of estimating machining time promptly and simply based on shape data without requiring any NC program is developed. The input data to the AI system are color information regarding the machined depths, which are used to estimate the rough-machining time, and color information regarding the machined surface curvature distributions to estimate the finish-machining time. Color information on the machined depths and machined surface curvature distributions is created using three-dimensional computer aided design (3D CAD) data. To build the AI system, the shape data and machining time data accumulated at the machining site are used, so that the machining time estimated reflects the machining method, machining expertise, and the machine tool characteristics employed.

Assessing first‐order BQART estimates for ancient source‐to‐sink mass budget calculations

AbstractConstraining the timing and volume of sediment dispersal in an ancient sedimentary system is vital to understand a basin's infill history. One preferred method for a first‐order approximation of ancient sediment load estimates, the BQART model, is based on empirical observations of modern river systems relating basin morphology, topography, climate, run‐off and bedrock characteristics. Despite the popularity of such methods, a comprehensive assessment on the validity of using modern river observations to measure sediment load on geological timescales is lacking. Here, we investigate the uncertainties, sensitivities and practicalities surrounding the use of modern empirical observations in general and the BQART model in particular, to evaluate ancient sediment river loads. Although catchment area and relief are the least constrained parameters in an ancient sedimentary system, the temperature parameter may have an even more significant impact in the range of predicted sediment load estimates using a BQART approach. The applicability of BQART is most suitable for regional to continental scale source‐to‐sink systems that are based on robust paleogeographic and paleoclimatic models of cold (&lt;2°C) or warm temperate (&gt;8°C) climates. One further needs to consider the high amplitude discharge events that can dominate the stratigraphic record which are not captured by historical observations of sediment load over a 30‐year period. In addition, our limited understanding of bedload material transport and an unknown pristine environment in the Anthropocene reduce the reliability of modern sediment load estimates for the ancient. Mass budget estimates in deep time based on empirical relationships of modern river systems can thus provide first‐order estimates within an order of magnitude but need to consider the limitations imposed by extrapolating the modern to the ancient. Here, we present a framework to consider the suitability of the BQART method for ancient source‐to‐sink mass budget analyses.