MétaCan
Menu
Back to cohort
Record W4309157237 · doi:10.1111/risa.13949

Holton et al., Characterization of asbestos exposures associated with the use of facial makeups. Risk Analysis, 42, 2129–2139

2022· letter· en· W4309157237 on OpenAlexaff
Murray M. Finkelstein

Bibliographic record

VenueRisk Analysis · 2022
Typeletter
Languageen
FieldChemical Engineering
TopicChemical Safety and Risk Management
Canadian institutionsMcMaster UniversityUniversity of Toronto
Fundersnot available
KeywordsAsbestosTalcAmphiboleEnvironmental chemistryEnvironmental scienceMineralogyGeologyChemistryMaterials scienceMetallurgyPaleontology

Abstract

fetched live from OpenAlex

Holton and colleagues have performed a risk assessment after measuring asbestos released from several samples of facial makeup. Unfortunately, it is not possible to interpret or generalize their findings because the authors have not described the source(s) of the talc tested or the asbestos concentrations of the samples. The concentration of amphiboles varies widely between sources, and the authors are urged to divulge the locations of the ore bodies providing the talc for their samples, as well as the asbestos concentration of the samples, so that the results may be interpreted and possibly generalized.

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)0.002
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.011
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0110.008
Insufficient payload (model declined to judge)0.0030.002

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 head0.014
GPT teacher head0.215
Teacher spread0.201 · 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 designObservational
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

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueRisk AnalysisSame topicChemical Safety and Risk ManagementFrench-language works237,207