Evaluation préliminaire du rendement d'un cépage hybride (Seyval blanc), en fonction de différents moyens de protection contre le gel hivernal au Québec
Bibliographic record
Abstract
Afin devaluer la qualite de differents types de protection hivernale, des donnees de temperatures au sol sous differents abris (butte de terre, butte de feuilles, neige rapportee et neige naturelle) et de l?air ambiant ont ete enregistrees. Les resultats montrent que le Seyval blanc, s?il n?est pas protege contre le gel hivernal, subit des dommages tres serieux lorsque la temperature de l?air atteint -30 °C. Toutes les methodes de protection experimentees ont permis de conserver des temperatures du sol plus elevees. Les resultats indiquent aussi que le taux de mortalite des bourgeons a fruits des ceps sans protection est presque de 100 %, par rapport aux ceps qui sont proteges dont le taux de mortalite des bourgeons a fruits se situe entre 22,5 et 35,8 %. Par contre, le rendement en raisin des ceps sans aucune protection hivernale est nul. Les meilleurs rendements en raisin ont ete obtenus sur les sites dont les ceps proteges par buttage (40 cm de terre). Les ceps proteges par la neige naturelle ainsi que par l?enfeuillage (30 cm + neige rapportee et sarments couches) ont porte les fruits avec la plus haute teneur en sucre. La neige est aussi un excellent isolant car une couche de neige de 37 cm d?epaisseur a permis la survie des ceps proteges par la neige meme lorsque la temperature a atteint -30 °C
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".