DL 45 : Application and validation of the general methodology and concept of groundwater vulnerability assessment at regional scale on the Israeli coastal aquifer
Bibliographic record
Abstract
This deliverable consists in applying the concepts and methodologies developed in Deliverable D43 and D44 to synthetic and real case studies. Our researches have focused on the generalized groundwater vulnerability assessment methodology, and more precisely on evaluating, under the framework of physically-based indicators, the groundwater sensitivity/vulnerability to stress factor considering artificial recharge as a potential response to the degradation of the groundwater resource. In the context, different approaches have been identified in the literature and implemented in appropriate modelling tools (i.e. HydroGeoSphere) for calculating the various sensitivity/vulnerability coefficients. These approaches are the influence coefficient method, the sensitivity equation method and the adjoint operator method. The two first methods show relevant results on both the considered synthetic case studies that relate groundwater vulnerability to (1) quantity issues and (2) to sea water intrusion. They illustrate the way of applying the methodology to “real case studies”.These first applications should be the object of more complex but strongly related case studies.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".