Capture map-based groundwater potential zonation
Bibliographic record
Abstract
We propose a novel methodology for the groundwater potential zonation with the integration of capture fraction in a multi-objective problem. Each hydrogeological feature such as lakes, ponds and river stretches are influenced by the groundwater extraction through pumping wells and the specific distance of the pumping wells from these features. Analysis of the capture is crucial in managing the quantity aspect of surface water & groundwater resources, as properly developed capture maps could help in taking safety measures for present and future water usage in the area. This paper discusses the methodology for the identification of groundwater potential areas based on capture maps and their application in water resources management. The optimum location of pumping wells has been identified based on the capture and pumping cost. The methodology has been applied on the lower Ain River basin, France. Using the prediction capabilities of the groundwater model, capture map for the river inflow, river outflow, storage-in, storage-out and drawdown were developed up to the year 2060 and the results were interpreted. Time series forecast algorithms were applied in predicting the water levels and flow into the river. Results show that leakage in was more dominated in the region considered in comparison to leakage out.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.002 | 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".