Croissance des chiens de grande race : relation avec l'état de santé
Bibliographic record
Abstract
Les courbes de croissance sont des outils primordiaux pour accompagner la croissance d’un chiot et s’assurer de sa santé future. En effet, cette période fondamentale a été liée à certains problèmes de santé, notamment des problèmes articulaires. Aucune courbe n’existant pour les chiens de plus de 45 kilogrammes, cette étude établit ces courbes pour quatre races géantes : le Landseer, le Leonberg, le Saint-Bernard et le Terre-Neuve. De plus amples recherches semblent nécessaires pour établir un lien entre la croissance et des pathologies articulaires mais cette étude montre que la prévalence d’ostéosarcome, celle de boiteries intermittentes chroniques et celle de rupture de LCCr augmente avec le gabarit du chien, la vitesse de croissance ou les deux. En revanche, aucun lien entre la croissance et la prévalence de dysplasie des hanches, des coudes et de SDTE n’a pu être objectivé par cette étude.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".