MétaCan
Menu
Back to cohort
Record W7099079447

HU MAN FACTORS, 1994,36(2),258-268 Extended Workdays in an Underground Mine: A Work Performance Analysis

2016· article· en· W7099079447 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldArts and Humanities
TopicSimone de Beauvoir and Sartre
Canadian institutionsnot available
Fundersnot available
KeywordsWork (physics)ScheduleWork scheduleShift workQuality (philosophy)Underground mining (soft rock)Data collection
DOInot available

Abstract

fetched live from OpenAlex

Many companies in different industrial sectors are exploring alternative work schedules to deal with diverse problems associated with shiftwork. The use of extended workday schedules (regular shift lengths exceeding 8 h with compressed workweeks) is attracting growing interest in many industries that use continuous operations. To address concerns regarding possible fatigue effects on safety and work performance associated with such schedules, the U.S. Bureau of Mines con-ducted a two-phase study at an underground metal mine in western Canada. Data were collected before and after a group of workers employed at the mine changed from an 8- to a 12-h schedule. Results indicate nearly unanimous acceptance and improved sleep quality associated with the new schedule. In general, fatigue-sensitive behavioral and physiological performance measures show either no change or improvement with 12-h shifts. We conclude that the extended workday schedule should be retained but periodically reevaluated.

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)0.001
metaresearch head score (Gemma)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score0.122

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0120.001

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 head0.037
GPT teacher head0.237
Teacher spread0.199 · 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
GenreEmpirical

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
Published2016
Admission routes1
Has abstractyes

Explore more

Same topicSimone de Beauvoir and SartreFrench-language works237,207