Assessing performance of a permeable biobarrier
Bibliographic record
Abstract
While the past 15 years have seen rapid development of permeable reactive barriers and other in situ systems for the treatment of groundwater contamination, assessing performance has not received the same attention. In some cases the long-screened monitoring wells installed as part of initial site investigations are used to monitor treatment, while in others a few additional wells may be installed to serve as sentinels. Performance is assessed based on differences in contaminant concentrations, which is unreliable where plumes are spatially complex and/or temporally unstable. In this work, a standard concentration performance metric is compared with that based on mass flux, using data collected during an evaluation of a discontinuous aerobic biobarrier treating a near-source (and spatially complex) BTEX plume. The data were derived from high-resolution multilevel samplers installed up- and down-gradient of the treatment system. Concentrations in screened monitoring wells of various lengths were approximated by interval-weighted concentrations from these multilevels. The accuracy of the different performance assessment methods was measured based on estimated uncertainty generated as a result of observed spatial plume complexity. While more data intensive, mass flux proved to be a more reliable performance metric. Assessment using data from few long-screened wells could not reliably match that of the flux fences, but did come within acceptable limits when the number of wells approached the number of multilevels.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".