Bibliographic record
Abstract
Voir, entendre, toucher, sentir, gouter, mais aussi penser, reflechir, decider, etc. Le cerveau nous permet de percevoir et d’interagir avec le monde qui nous entoure. Il regroupe plus de dizaines de milliards de cellules, organisees en circuits qui recoivent, integrent et transmettent constamment des signaux chimiques et electriques. Il est le siege de notre perception, de nos actes et de notre conscience. A l’inverse de l’apparente simplicite des circuits electroniques imprimes, l’organisation des circuiteries de notre cerveau est loin d’avoir revele tous ses secrets. Malgre les progres techniques des dernieres decennies, nous ne savons pas, ou peu, comment le cerveau humain elabore nos emotions, notre pensee ou nos comportements. Cette impuissance/meconnaissance se traduit par des traitements sub-optimaux des pathologies neurologiques ou neuropsychiatriques. Face a ce challenge de taille, plusieurs grands projets ont ete recemment lances, tels le “Human Brain Project” (Europe) et le “BRAIN project” (USA), qui visent a simuler, enregistrer ou controler l’activite de circuiteries cerebrales afin de mieux comprendre le fonctionnement du cerveau humain. Pour cela, les neurosciences experimentales et cliniques reposent sur le developpement de techniques futuristes, parfois dignes des meilleurs romans de science-fiction, dont fait partie l’optogenetique.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.033 |
| Scholarly communication | 0.010 | 0.020 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 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".