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Record W4387967368 · doi:10.1186/s12940-023-01025-3

Correction: Health consequences of exposure to aircraft contaminated air and fume events: a narrative review and medical protocol for the investigation of exposed aircrew and passengers

2023· review· en· W4387967368 on OpenAlexaff
Jonathan Burdon, Lygia Therese Budnik, Xaver Baur, G. Hageman, C. V. Howard, Leonie Coxon, Clement E. Furlong, David Gee, Tristan Loraine, Alvin V. Terry, John Midavaine, Hannes Petersen, Denis Bron, Colin L. Soskolne, Susan Michaelis

Bibliographic record

VenueEnvironmental Health · 2023
Typereview
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAircrewAeronauticsProtocol (science)Narrative reviewEnvironmental healthContaminationNarrativeMedicineMedical emergencyEnvironmental scienceForensic engineeringEngineeringIntensive care medicineAlternative medicinePathologyBiologyArt

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)0.012
metaresearch head score (Gemma)0.092
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: Review · Consensus signal: none
Teacher disagreement score0.036
Threshold uncertainty score0.121

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.092
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.004
Bibliometrics0.0050.004
Science and technology studies0.0010.002
Scholarly communication0.0030.003
Open science0.0030.003
Research integrity0.0060.006
Insufficient payload (model declined to judge)0.0360.009

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 head0.095
GPT teacher head0.406
Teacher spread0.311 · 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
GenreReview

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
Published2023
Admission routes1
Has abstractno

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