MétaCan
Menu
← Back to cohort
Record W6950617641 · doi:10.5683/sp3/aiahgw

Scénario 69_3

2025· dataset· fr· W6950617641 on OpenAlexaff

Bibliographic record

VenueBorealis · 2025
Typedataset
Languagefr
FieldMedicine
TopicAdvanced Neuroimaging Techniques and Applications
Canadian institutionsUniversity of Calgary
Fundersnot available
Keywords3d simulation3d modelFrame (networking)

Abstract

fetched live from OpenAlex

Lors de la simulation virtuelle suivante, vous interagirez avec des personnages et dans une situation basée sur des situations cliniques réelles. La simulation virtuelle favorise un environnement d'engagement actif dans un espace sécuritaire. En tant que créateurs de la simulation virtuelle, nous avons fait le maximum pour rendre la simulation aussi réelle que possible. Nous reconnaissons que certains aspects ne sont pas réalistes. En tant qu'apprenants, nous vous demandons de vous engager dans la simulation et de considérer les membres de l'équipe soignante ainsi que le patient et sa famille, comme étant réels. Votre engagement dans l’expérience de simulation virtuelle permettra d’optimiser vos apprentissages.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.946
Threshold uncertainty score0.181

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0540.041

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 head0.054
GPT teacher head0.373
Teacher spread0.319 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreDataset

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
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueBorealis→Same topicAdvanced Neuroimaging Techniques and Applications→French-language works237,207→