Bibliographic record
Abstract
Pourquoi Léonard de Vinci a-t-il, notamment, placé des anguilles, des harengs et des oranges sur la table de sa représentation de la Dernière Cène ? Pourquoi y fait-il figurer une salière renversée devant Judas ? Sachant qu’il ne sera plus possible de connaître les intentions de l’artiste, les auteurs mettent à l’épreuve quatre hypothèses qui peuvent nous aider à comprendre la valeur de chacun des aliments figurant sur la peinture. Tour à tour, ils évaluent si les aliments correspondent à ceux mentionnés dans les récits du Nouveau Testament, à ceux que Léonard aimait manger ; si leur goût ou leur nom leur donne une valeur symbolique particulière. Leur parcours rigoureux à travers l’exégèse biblique, les biographies de Léonard de Vinci, l’histoire médiévale de l’alimentation et la sémiotique de l’image, leur permet de conclure que le maître du sfumato a probablement « enfumé » celles et ceux qui ont contemplé son Cenacolo, en leur proposant une oeuvre en « trompe-la-bouche ».
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".