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Record W2983587674 · doi:10.26616/nioshpub2013144

Health and safety of young workers: proceedings of a U.S. and Canadian series of symposia.

2013· report· en· W2983587674 on OpenAlexafffundabout
Carol W. Runyan, John Lewko, Kimberly J. Rauscher, Dawn N. Castillo, Sara Brandspigel, John Howard, Robert A. Blum, Letitia Davis, Richard Volpe, F Curtis Breslin, Pete Smith, Beatriz Pazos, May Sudhinaraset, Susan S. Gallagher, Sara Rattigan, P. Levesque, Jeremy Staff, John E. Schulenberg, Jerald G. Bachman, Michael L. Parks, Matthew VanEseltine, Jeylan T. Mortimer

Bibliographic record

Venuenot available
Typereport
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsLaurentian University
FundersNational Institute of Child Health and Human DevelopmentNational Institute on Drug AbuseOntario Neurotrauma FoundationNational Institute for Occupational Safety and HealthInjury Prevention Research CenterNational Science Foundation
KeywordsSeries (stratigraphy)EngineeringLibrary scienceComputer scienceGeology

Abstract

fetched live from OpenAlex

These Proceedings do not constitute endorsement of the views expressed or recommendations by the National Institute for Occupational Safety and Health (NIOSH).The opinions and conclusions expressed in the articles are those of each author and not necessarily those of NIOSH.All authors were provided the opportunity to review, update and correct statements attributed to them in these Proceedings.Recommendations are not final statements of NIOSH policy or of any agency or individual involved.They are intended to be used in advancing the knowledge needed for improving young worker safety and health.

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)0.004
metaresearch head score (Gemma)0.002
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: Other · Consensus signal: none
Teacher disagreement score0.431
Threshold uncertainty score0.867

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0030.001
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0210.005

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 head0.080
GPT teacher head0.438
Teacher spread0.358 · 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

Citations12
Published2013
Admission routes3
Has abstractyes

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