MétaCan
Menu
Back to cohort
Record W2811350277 · doi:10.7892/boris.69556

Neurosciences modernes : avec ou sans l’optogénétique ?

2015· article· fr· W2811350277 on OpenAlexaff
Antoine Adamantidis

Bibliographic record

VenueBern Open Repository and Information System (University of Bern) · 2015
Typearticle
Languagefr
FieldNeuroscience
TopicPhotoreceptor and optogenetics research
Canadian institutionsDouglas Mental Health University InstituteMcGill University
Fundersnot available
KeywordsHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.405
Threshold uncertainty score0.965

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.007
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.053
GPT teacher head0.262
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueBern Open Repository and Information System (University of Bern)Same topicPhotoreceptor and optogenetics researchFrench-language works237,207