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Record W3031130303 · doi:10.3747/co.27.6245

The Canadian Cancer Research Conference 2019

2020· article· en· W3031130303 on OpenAlexaffvenueabout
Kristianne J.C. Galpin, David P. Cook, L.M. Salemi, Sara Urowitz, Claire Williams, John C. Bell, Michael Brundage, Barbara C. Vanderhyden

Bibliographic record

VenueCurrent Oncology · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsOntario Institute for Cancer ResearchQueen's UniversityOttawa HospitalCanadian Partnership Against CancerUniversity of Ottawa
Fundersnot available
KeywordsMedicinePresentation (obstetrics)Medical educationAlternative medicineResearch programFamily medicineLibrary sciencePathology

Abstract

fetched live from OpenAlex

The 5th Canadian Cancer Research Conference (ccrc) took place 3-5 November 2019 in Ottawa, Ontario. Nearly 1000 participants-scientists, oncologists, community members, and patients-gathered to share knowledge, foster collaboration, and fuel the future of cancer research in Canada. The scientific program included 3 plenary sessions, 26 concurrent sessions, and 2 poster sessions presenting research described in more than 600 submitted abstracts, giving participants the opportunity to share health research that collectively encompassed the 4 pillars recognized by the Canadian Institutes of Health Research. In addition to the breadth of topics addressed by Canadian and international experts, the highlights of the meeting included the integration of patients and patient advocates, new rapid-fire sessions for abstract presentation, and events that enhanced learning opportunities for trainees.

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)0.008
metaresearch head score (Gemma)0.011
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 score0.768
Threshold uncertainty score0.491

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0040.003
Science and technology studies0.0070.002
Scholarly communication0.0100.002
Open science0.0030.005
Research integrity0.0070.007
Insufficient payload (model declined to judge)0.1130.043

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 head0.191
GPT teacher head0.450
Teacher spread0.260 · 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

Citations4
Published2020
Admission routes3
Has abstractyes

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