Échanges napperivière en vallée alpine : quantification et modélisation (Vallée d'Aoste, Italie)
Bibliographic record
Abstract
A mathematical model is applied to the Aoste Valley (Italy), which is a good example of the hydrological workings of alluvial rock basin aquifers in mountainous regions. The course of the Dorea Baltea follows the valley, which between the altitudes of 500 and 600 m is dominated by summits reaching more than 3000 m. The lacustrine- and fluviatile-type sedimentary deposits are accompanied by several alluvial cones that, due to their varying nature, confer very diverse hydrodynamic characteristics to the land. In this particular context, the source and drainage of the Dorea control the hydrodynamics of the aquifers and influence the spatial dispersion of the physicochemical properties of the groundwater. The model also led to quantify and determine the zones and types of exchanges with the river. Simulation of lowering the water table in the river showed the variable sensitivity of the water table piezometry with various spatial impacts. Finally, the streamaquifer impact is highlighted by the spatial evolution of the sulphate contents coming from deep lateral sources. Taking into account the streamaquifer exchanges in an underground-flow mathematical model gives a better understanding of the workings of the valley aquifers and thus their management, especially for the development of watercourses in mountainous regions.[Journal translation]
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".