Modélisation d'un système karstique complexe (bassin de S<sup>t</sup>-Chaptes, Gard, France) : un outil de synthèse des données géologiques et hydrogéologiques
Bibliographic record
Abstract
Numerous software packages allow the efficient modeling of the hydrodynamic behaviour of aquifers in continuous media. To study pressure transfer in discontinuous media like karsts, the black-box models are restrictive and the models that consider discrete conduit networks are unsuitable for reservoir scale. We show that the utilization of a continuous media model can lead to useful results, even in the case of complex systems, but needs to be adapted to karst specificity. The problem is approached by studying a hydrogeological system located in the Mediterranean Languedoc region: the St-Chaptes basin. This system consists of three superposed aquifers included in four different stratigraphic series. The main aquifer is a karst formation in contact with two other karst formations that belong to different hydrogeologic systems. Considering geological data in addition to hydrological data and with the hypothesis of a relative homogenization of the karst's hydraulic behaviour on a large spatial scale for daily to monthly increments, the model that takes into account the relations with the other aquifers allows (i) a preliminary identification of the main heterogeneities inside the reservoir; (ii) the location of barriers and low-permeability zones that isolate some parts of the aquifer; (iii) the observation of a curious behaviour of the piezometric levels in the confined zones of the aquifer; and (iv) the characterization of the exchanges with the other low-volume but existing aquifers.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".