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Record W4296261087 · doi:10.1503/cmaj.220577

A better normal in Canada will need a better detection system for emerging and re-emerging respiratory pathogens

2022· article· en· W4296261087 on OpenAlexvenueaboutno aff
Isha Berry, Kevin A. Brown, Sarah A. Buchan, Karin Hohenadel, Jeffrey C. Kwong, Samir Patel, Laura C. Rosella, Sharmistha Mishra, Beate Sander

Bibliographic record

VenueCanadian Medical Association Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicData-Driven Disease Surveillance
Canadian institutionsnot available
Fundersnot available
KeywordsPandemicOutbreakPublic healthCoronavirus disease 2019 (COVID-19)Middle East respiratory syndromePublic health surveillance2019-20 coronavirus outbreakInfectious disease (medical specialty)Disease surveillanceMedicineComputer scienceData scienceEnvironmental healthDiseaseVirologyPathology

Abstract

fetched live from OpenAlex

KEY POINTS Infectious disease surveillance is fundamental to public health systems.[1][1] However, Canada’s COVID-19 pandemic response relied on clinical and outbreak management (COM) platforms; i.e., public health institutions tracking case counts across a range of reasons for testing (e.g.,

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)0.007
metaresearch head score (Gemma)0.021
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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.937
Threshold uncertainty score0.460

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.021
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.003
Science and technology studies0.0060.002
Scholarly communication0.0070.005
Open science0.0040.004
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0420.011

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 head0.006
GPT teacher head0.214
Teacher spread0.208 · 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
GenreCommentary

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

Citations12
Published2022
Admission routes2
Has abstractyes

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