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Record W4280621874 · doi:10.3917/rdna.hs07.0089

De la formation initiale à la projection opérationnelle : l’apprentissage de soi-même et le suivi de son sommeil par la méthode d’ORFA

2022· article· fr· W4280621874 on OpenAlexaff
Jean-Philippe Robert, Mickaël Ravel

Bibliographic record

VenueRevue Défense Nationale · 2022
Typearticle
Languagefr
FieldNeuroscience
TopicPain Management and Placebo Effect
Canadian institutionsCanadian Armed Forces
Fundersnot available
KeywordsHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

Le manque de sommeil a un impact négatif sur la cognition, les apprentissages ou encore sur la prise de décision. Le futur chef doit en prendre conscience et mettre en place des stratégies adaptées. Des moniteurs et instructeurs ORFA (Optimisation des ressources des forces armées), spécialisés sur les questions du stress et du sommeil, apportent ainsi des clés de compréhension et des outils efficients afin de répondre efficacement. La FI-ORFA (Formation initiale-ORFA) proposée aux élèves apporte donc des bases fondamentales pour se préparer psychologiquement à n’importe quelle situation. À l’heure du développement des forces morales pour faire face aux conflits de haute intensité, la méthode ORFA semble être un outil pertinent pour optimiser les capacités de chacun.

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)0.015
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.681
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.045
GPT teacher head0.296
Teacher spread0.251 · 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.

Study designSimulation or modeling
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
Published2022
Admission routes1
Has abstractyes

Explore more

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