Comment co-construire des indicateurs pour la durabilité des nappes captives ?
Bibliographic record
Abstract
Dans le cadre du projet DEESAC (Durabilité et exploitabilité des eaux souterraines des aquifères captifs ou sous couverture) financé par le programme OneWater, ce webinaire présente trois cas de gestion concertée des aquifères captifs en France et à l'international. Les trois cas présentés sont les suivants : - Les nappes de l'Astien (Hérault, France) - Le Grand Bassin Artésien (Australie) - Les nappes profondes de Gironde (France) L'étude menée dans le cadre du projet a permis d'analyser sur chaque cas : (i) l'historique de la gestion concertée des nappes captives et la façon dont les différents acteurs ont été progressivement impliqués, (ii) comment les objectifs de gestion durable ont été définis collectivement, ainsi que les indicateurs de bon état mis en place pour répondre à ces objectifs de gestion durable. La présentation a été suivie d'un temps d'échange entre gestionnaires, chercheurs et partenaires du projet.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.002 |
| 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".