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Record W3155664545 · doi:10.24095/hpcdp.41.4.03f

Avis de publication – Outil de données sur le Cancer chez les jeunes au Canada : derniers taux d’incidence et nombres de cas

2021· article· fr· W3155664545 on OpenAlexaffvenueabout
Lin Xie, Prinon Rahman, Meghan Laverty, Manal Salibi, Mylène Fréchette, Jaskiran Kaur, Sulaf Elkhalifa, Owen Wesley Smith-Lépine, Tony Bebawy, Scott van Millingen, Jay Onysko

Bibliographic record

VenuePromotion de la santé et prévention des maladies chroniques au Canada · 2021
Typearticle
Languagefr
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsPublic Health Agency of Canada
Fundersnot available
KeywordsHumanitiesMedicineArt

Abstract

fetched live from OpenAlex

L’Agence de la santé publique du Canada a publié une mise à jour de l’outil de données sur le Cancer chez les jeunes au Canada, qui contient désormais les derniers taux d’incidence et nombres de cas de cancer.

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)0.019
metaresearch head score (Gemma)0.104
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.095
Threshold uncertainty score0.192

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.104
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0140.020
Science and technology studies0.0030.002
Scholarly communication0.0090.002
Open science0.0020.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0150.002

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 head0.041
GPT teacher head0.329
Teacher spread0.287 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2021
Admission routes3
Has abstractyes

Explore more

Same venuePromotion de la santé et prévention des maladies chroniques au CanadaSame topicGlobal Cancer Incidence and ScreeningFrench-language works237,207