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Record W2163425927 · doi:10.1518/155723408x299870

Railroad Human Factors

2007· article· en· W2163425927 on OpenAlexaboutno aff
Elisabeth Sussman, Thomas G. Raslear

Bibliographic record

VenueReviews of Human Factors and Ergonomics · 2007
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)LegislationHuman factors and ergonomicsEngineeringAlertnessHuman engineeringProductivityTransport engineeringRelevance (law)Cover (algebra)Poison controlBusinessRisk analysis (engineering)Political scienceLawPsychologyEconomics

Abstract

fetched live from OpenAlex

The purpose of this chapter is to introduce the reader to the human factors concerns of the railroad enterprise as it exists in the United States and Canada and to provide an overview of human factors research related to railroads. The railroad enterprise is complex and, in many ways, distinct from other forms of transportation. Differences found between railroading and other modes are arguably more profound in internal organization and tradition than in equipment and technologies. Because of this, a significant portion of this chapter is given over to a description of context. Understanding this context is critical in understanding the need for and relevance of the human factors research cited. We also cover research concerns and efforts in a number of key areas, including safety legislation versus regulation, safety and productivity, perceived versus measured safety, close call confidential reporting, the social environment of the U.S. railroad industry, operator fatigue and alertness, locomotive ergonomics and cab design, the locomotive engineers' and dispatchers' roles, remotely controlled locomotives, and trespassing and grade-crossing accidents.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.114
Threshold uncertainty score0.910

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.182
GPT teacher head0.491
Teacher spread0.309 · 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 teacher head, 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

Citations14
Published2007
Admission routes1
Has abstractyes

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