Conditioned backward probability modeling to identify sources of groundwater contaminants subject to sorption and decay
Bibliographic record
Abstract
If contamination is observed in an aquifer, a backward probability model can be used to obtain information about the former position of the observed contamination. A backward location probability density function (PDF) describes the possible former positions of the observed contaminant particle at a specified time in the past. If the source release time is known or can be estimated, the backward location PDF can be used to identify possible source locations. For sorbing solutes, the location PDF depends on the phase (aqueous or sorbed) of the observed contamination and on the phase of the contamination at the source. These PDFs are related to adjoint states of aqueous and sorbed phase concentrations. The adjoint states, however, do not take into account the measured concentrations. Neupauer and Lin (2006) presented an approach for conditioning backward location PDFs on measured concentrations of non‐reactive solutes. In this paper, we present a related conditioning method to identify the location of an instantaneous point source of a solute that exhibits first‐order decay and linear equilibrium or non‐equilibrium sorption. We derive the conditioning equations and present an illustrative example to demonstrate important features of the technique. Finally, we illustrate the use of the conditioned location PDF to identify possible sources of contamination by using data from a trichloroethylene plume at the Massachusetts Military Reservation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".