Modèle d’explication de flux à composantes d’erreurs spatialement corrélées
Bibliographic record
Abstract
Dans cette étude, nous proposons une généralisation de la formulation à composantes d’erreurs qui permet de représenter différents effets explicatifs de la présence de corrélation dans les erreurs de modèles de régression avec données de flux. Selon la formulation proposée, le terme d’erreur se décompose en une somme d’une erreur relative à la zone d’origine, une erreur relative à la zone de destination et une erreur associée au flux. Chaque composante d’erreur est issue d’un processus générateur auto-régressif spatial d’ordre 1. L’estimation des paramètres du modèle est basée sur la méthode du maximum de vraisemblance. La méthodologie proposée a l’avantage de demeurer applicable même dans le contexte d’échantillons de grande taille.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".