Bibliographic record
Abstract
Nous étudions les chaînes de Markov branchantes sur un espace d’états (espace de types) dénombrable X en mettant l’accent sur les aspects qualitatifs du comportement limite de l’évolution des distributions empiriques de la population. Aucune condition n’est imposée sur les distributions multitypes des descendants des points de X autre que d’avoir la même moyenne et de satisfaire à une condition de moment de type LlogL. Nous montrons que la martingale de population résultante est uniformément intégrable. Ensuite, nous établissons le lien entre la convergence des moyennes empiriques de la chaîne branchante et les espaces stationnaires de la chaîne de Markov ordinaire associée sur X (supposée irréductible et transiente). Notre résultat principal est la convergence presque sûre des distributions empiriques vers une mesure de probabilité aléatoire sur le bord d’une compactification appropriée de X. Les considérations finales portent sur l’interaction générale entre les bords mesurables de la chaîne branchante et de la chaîne ordinaire associée.
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".