MétaCan
Menu
Back to cohort
Record W4288766885 · doi:10.1002/9781119887638.ch9

Physical Hazards – Light, Heat, Noise, Vibration, Pressure and Radiation

2022· other· en· W4288766885 on OpenAlexaff
Professor Kerry Gardiner, Professor David Rees, Professor Anil Adisesh, Professor David Zalk, Professor Malcolm Harrington, Dr Roxane Gervais, Professor Joan Saary

Bibliographic record

Venuenot available
Typeother
Languageen
FieldMedicine
TopicSpaceflight effects on biology
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDaylightArtificial lightVibrationLight fieldAcousticsNoise (video)EardrumEnvironmental scienceLuminanceComputer scienceOpticsPhysicsArtificial intelligence

Abstract

fetched live from OpenAlex

Many countries have regulations that relate to light in the working environment. Surrounding the task will be an immediate field and, in the background, will be yet another field, both of which emit light at a certain luminance. No artificial lamp reproduces exactly the combination of light wavelengths that is found with daylight, and therefore colours seen under artificial light may appear to be different from those illuminated naturally. A full lighting survey will provide details of any defects in the lighting system and of any potentially acute or chronic occupational health problems. Diving in a full suit or working in hot, humid conditions can greatly alter this homeostasis. Sound involves pressure changes in the air that are picked up by the eardrum and transmitted to the brain. Vibration is oscillatory motion about a point.

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)0.000
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.048
Threshold uncertainty score0.161

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0480.010

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 head0.004
GPT teacher head0.253
Teacher spread0.249 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same topicSpaceflight effects on biologyFrench-language works237,207