Quelques principes de normalisation des noms botaniques français : les arbres d’Amérique du Nord1
Bibliographic record
Abstract
Le présent article porte sur la normalisation des noms botaniques français désignant les arbres indigènes d’Amérique du Nord. Il propose une nomenclature normalisée française, inspirée des noms scientifiques latins et des noms populaires français. Ses quatre principes de base sont : le caractère binomial du nom, son exclusivité, l’exactitude de sa signification botanique et son universalité. Sont également analysés d’autres principes comme la priorité du nom existant sur le néonyme ainsi que le recours aux grandes catégories conceptuelles, telles que la morphologie, la ressemblance avec d’autres taxons, la toponymie, l’écologie, l’anthroponymie et l’utilisation de la plante. Enfin, un aperçu du travail qui reste à faire pour normaliser la totalité des noms français de la dendroflore mondiale vient conclure l’article.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".