Bibliographic record
Abstract
Malgré ses progrès, la recherche biomédicale demeure incapable de donner une explication claire et consensuelle de l’augmentation de l’incidence de certaines maladies. Prenons le cas des cancers : si la grande majorité des cancers du poumon sont, sans aucune ambiguïté, attribuables à la consommation de tabac, l’augmentation de la fréquence des lymphomes malins non hodgkiniens, des tumeurs cérébrales, du cancer du pancréas, de la prostate, du cancer du sein ou des leucémies de l’enfant reste mal comprise. L’incertitude pèse notamment sur le rôle de l’environnement. Les expositions différentielles aux substances chimiques contenues dans l’eau, les sols, l’air ou l’alimentation, que ce soit dans la sphère privée, les lieux publics ou en milieu professionnel, ne sont-elles pas plus largement en cause qu’on l’a supposé jusqu’à présent ? Pour le savoir, il est nécessaire d’appliquer la démarche de précaution et d’orienter la recherche scientifique, seule à même d’apporter des données objectives.
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 | 0.067 | 0.148 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.011 | 0.016 |
| Insufficient payload (model declined to judge) | 0.047 | 0.012 |
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".