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Record W1546076657

2005 award recipients

2006· article· en· W1546076657 on OpenAlexaboutno aff
Cal Gutkin

Bibliographic record

VenueEurope PMC (PubMed Central) · 2006
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsPrivilege (computing)Library scienceFoundation (evidence)Work (physics)Value (mathematics)Medical educationFamily medicineMedicinePolitical scienceLawComputer scienceEngineering
DOInot available

Abstract

fetched live from OpenAlex

Once a year it is my privilege to present the names of recipients of the College of Family Physicians of Canada’s (CFPC’s) Honours and Awards. Supported by contributions to the CFPC’s Research and Education Foundation (REF)—the only charitable organization in Canada dedicated solely to supporting the work of family physicians—these awards recognize and support family doctors across Canada for their outstanding work and their ongoing commitment to education and research. As donations to the REF grow, so too do the number and value of the grants and scholarships awarded. We are, therefore, very grateful to the hundreds of family physicians whose contributions have helped build this important awards program. We congratulate each of the recipients listed below. The College and your colleagues are proud of you.

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)0.009
metaresearch head score (Gemma)0.030
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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.240
Threshold uncertainty score0.802

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.030
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0050.001
Scholarly communication0.0080.002
Open science0.0020.007
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.2400.127

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 head0.038
GPT teacher head0.332
Teacher spread0.294 · 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
GenreOther

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
Published2006
Admission routes1
Has abstractyes

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