Field-Scale Evaluation of the Passive Flux Meter for Simultaneous Measurement of Groundwater and Contaminant Fluxes
Bibliographic record
Abstract
A new method, passive flux meter (PFM), has been developed and field-tested for simultaneously measuring contaminant and groundwater fluxes in the saturated zone at hazardous waste sites. The PFM approach uses a sorptive permeable medium placed in either a borehole or monitoring well to intercept contaminated groundwater and release "resident" tracers. The sorbent pack is placed in a groundwater flow field for a specified exposure time and then recovered for extraction and analysis. By quantifying the mass fraction of resident tracers lost and the mass of contaminant sorbed, groundwater and contaminant fluxes are calculated. Here, we assessed the performance of PFMs at the Canadian Forces Base Borden field site in Ontario, Canada. Two field tests were conducted under imposed groundwater flow fields: (1) radial flow to a well and (2) linear flow in a test channel confined by sheet pile walls on three sides. Both tests demonstrate that the local fluxes measured by PFM and averaged overthe screen interval were within 15% of imposed groundwaterflow and within 30% of measured contaminant mass flux. Patterns in depth variations in groundwater and contaminant fluxes, determined by the PFM approach, allow for site characterization at a higher spatial resolution. These results support the PMF method as a potential innovative alternative for measuring groundwater and contaminant fluxes in screened wells.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".