Les enjeux érosifs dans le vignoble patrimonial à fortes pentes de Banyuls-sur-Mer (France)
Bibliographic record
Abstract
Dans le vignoble de Banyuls-sur-Mer, au sein des montagnes des Albères dans les Pyrénées orientales, les pentes très fortes et les précipitations méditerranéennes aboutissent à une forte érosion qui préoccupe les vignerons depuis des siècles. Méthodologiquement, à l’aide de modèles numériques de surface puis de terrain obtenus avec un drone par photogrammétrie, géoréférencés par des positionnements GNSS de précision centimétriques, nous pouvons mesurer la quantité de terrain érodé entre deux missions, et les mettre en regard avec les précipitations. A partir de deux zones tests aménagées différemment face à l’érosion, nous avons pu faire des premiers calculs et les comparer à des études empiriques anciennes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".