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Record W4381234490 · doi:10.23880/aii-16000138

Why and how should we use Artificial Intelligence, Machine Learning, and Deep Learning Approaches Differently on COVID-19 Coronavirus and Other Pathogens Research?

2021· article· en· W4381234490 on OpenAlexaff
Cheng JTJ

Bibliographic record

VenueAnnals of Immunology & Immunotherapy · 2021
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 diagnosis using AI
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakCoronavirusArtificial intelligencePandemicDeep learningComputer scienceVirologyBiologyMedicineInfectious disease (medical specialty)Outbreak

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.033
metaresearch head score (Gemma)0.063
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.967
Threshold uncertainty score0.174

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0330.063
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0030.002
Science and technology studies0.0020.006
Scholarly communication0.0090.016
Open science0.0020.003
Research integrity0.0050.012
Insufficient payload (model declined to judge)0.0020.002

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 head0.394
GPT teacher head0.434
Teacher spread0.040 · 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.

Study designTheoretical or conceptual
DomainMethods
GenreMethods

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 routes1
Has abstractno

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