Bibliographic record
Abstract
Preface de Francis Halle De minuscules etres unicellulaires savent resoudre des labyrinthes complexes ; des abeilles, dont le cerveau a la taille d'une tete d'epingle, sont capables de comprendre des concepts abstraits ; certaines plantes parasites comme les cuscutes peuvent evaluer le contenu nutritionnel de leurs victimes avant de decider de s'y installer... Comment nommer ces comportements ? Les humains sont-ils les seuls a posseder une « intelligence » et a prendre des decisions rationnelles en toute autonomie ? L'auteur montre que les bacteries, les plantes, les animaux et les autres formes de vie non humaines font preuve d'une etonnante disposition a faire des choix determinant leurs actions. Il nous emmene dans un voyage extraordinaire ? de la foret amazonienne aux laboratoires hi-tech ? a la rencontre de guerisseurs traditionnels et de scientifiques de pointe qui explorent les sciences du vivant. Cette nouvelle edition integre notamment une preface de Francis Halle qui prolonge la reflexion de Jeremy Narby sur la nature de l'intelligence des plantes.Jeremy Narby est un anthropologue canadien diplome de l'universite de Stanford (Californie). Il a passe plusieurs annees dans la foret amazonienne peruvienne et s'investit aujourd'hui aupres de l'organisation d'entraide Nouvelle Planete pour la defense des peuples indigenes. Il vit actuellement dans le Jura.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".