Time‐lapse electrical resistivity monitoring of salt‐affected soil and groundwater
Bibliographic record
Abstract
In order to develop and test a methodology for incorporating time‐lapse electrical resistivity imaging (ERI) into the monitoring of salt‐affected soil and groundwater, a multifaceted study including time‐lapse electrical resistivity imaging, push tool conductivity (PTC), and core analysis was conducted to monitor the movement of a saline contaminant plume over the span of 3 years. The survey was done on a field site containing salt‐affected soils and groundwater to depths of over 7 m. The site contained a tile drain system at approximately 2 m below ground level. Temperature and saturation changes were accounted for in electrical conductivity (EC) measurements to isolate changes in electrical conductivity due to changes in salt distribution. ERI inversion parameters were selected so that the inverse models gave the best match to PTC depth profiles and the best correlation with core EC data. A strong correlation between the core data and the ERI results was observed. Time‐lapse ERI difference images showed that the subsurface EC distribution was consistent with preferential solute removal above the tile drains in some locations. The ERI‐delineated reduction in solute concentration is consistent with nonuniform flushing due to depression‐focused recharge. The addition of time‐lapse ERI to the study allowed delineation of details of solute redistribution that would not have been possible with point measurements alone.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".