Bibliographic record
Abstract
Malgré la longue tradition de monuments funéraires qu’a connue la ville de Rome, nous possédons très peu d’information sur les tombeaux qui y furent érigés au haut Moyen Âge, c’est-à-dire entre le moment où l’on cessa d’utiliser les catacombes comme lieu de sépulture et le supposé renouveau de monuments complexes produits par Arnolfo di Cambio et les Cosmati au xiiie siècle. Toutefois, des fragments dispersés de peinture et d’architecture qui subsistent suggèrent qu’il existait à Rome une tradition continue de monuments sépulcraux depuis la fin de la période classique jusqu’à la fin du Moyen Âge. Cela est surtout évident dans le cas de l’arcosolium des catacombes. Grâce à des sources écrites et à des fragments de peinture murale, nous savons que ce type de monument a été repris dans les églises médiévales de Rome, comme l’ont été les autres formes de sépulture des catacombes.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".