Changer nos habitudes de prédation : l’exemple de la loutre et du pisciculteur
Bibliographic record
Abstract
Notre étude de cas porte sur l’aménagement d’une pisciculture subissant les prélèvements de loutres avides de poissons. Cette expérience est l’occasion de nouveaux apprentissages entre des acteurs agissant habituellement depuis des univers différents. Comprendre toutes les astuces qui sont devenues nécessaires pour une cohabitation réussie entre être humain et loutre suppose de suivre, au plus près, les expérimentateurs et les tourments qu’ils traversent (Stengers, 2000). Pour qu’une espèce puisse se déplacer librement, tel est le paradoxe, il faut multiplier les aménagements techniques qui lui permettront de vivre à « l’état naturel » (Micoud, 1993). Nous montrerons alors que tous les protagonistes ont changé, car ils ont appris de et par ce dispositif. Chacun a modifié ses habitudes pour vivre en paix et créer ainsi l’espace d’une cohabitation.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".