MétaCan
Menu
Back to cohort
Record W3034715224 · doi:10.1503/cmaj.1095875

Is Canada ready for the second wave of COVID-19?

2020· article· en· W3034715224 on OpenAlexvenueaboutno aff
Lauren Vogel

Bibliographic record

VenueCanadian Medical Association Journal · 2020
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsnot available
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakPrime ministerSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Prime (order theory)Race (biology)Third waveCoronavirusPolitical scienceVirologyMedicineDiseaseSociologyPoliticsLawGender studiesPolitical economyPathologyMathematicsInfectious disease (medical specialty)OutbreakCombinatorics

Abstract

fetched live from OpenAlex

Canada is past the worst of the first wave of coronavirus disease 2019 (COVID-19) cases, but according to Prime Minister Justin Trudeau and provincial health officials, a second wave is inevitable. Some provinces appear to be more prepared than others. Meanwhile, the global race to develop and

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)0.005
metaresearch head score (Gemma)0.017
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: Empirical · Consensus signal: none
Teacher disagreement score0.932
Threshold uncertainty score0.492

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0220.007
Scholarly communication0.0100.006
Open science0.0030.003
Research integrity0.0100.012
Insufficient payload (model declined to judge)0.0370.005

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 head0.079
GPT teacher head0.407
Teacher spread0.328 · 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
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

Citations17
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Medical Association JournalSame topicGlobal Health Workforce IssuesFrench-language works237,207