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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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; both teacher heads agree on what is shown here.
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".