Comparaison de techniques d’identification des Erwinia et des Pseudomonas responsables de la pourriture molle
Bibliographic record
Abstract
Trois méthodes, soit la caractérisation physiologique, l'utilisation de systèmes miniaturisés d'identification (API 20E, API NFT et Biolog) et l'analyse du profil électrophorétique des protéines sécrétées, ont été expérimentées afin de déterminer une technique précise et rapide d'identification des Pseudomonas et des Erwinia responsables de la pourriture molle. L'analyse des patrons électrophorétiques des protéines sécrétées est une méthode très efficace pour identifier les différentes espèces pectinolytiques de Pseudomonas fluorescents. Le système Biolog reconnaît efficacement le P. marginalis et le P. viridiflava. Le système API NFT est efficace pour l'identification du P. marginalis, du P. viridiflava et du P. syringae. C'est le système API 20E qui s'est avéré le plus efficace pour l'identification des Erwinia. L'électrophorèse des protéines sécrétées et le système API NFT permettent une identification rapide et efficace des Pseudomonas, tandis que pour les Erwinia, seul le système API20E est performant.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".