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Record W4239684109 · doi:10.1037/e572172013-474

Analysis of the Interaction between Human Operator and Automated Dispatch in Haul Truck Scheduling

2012· dataset· en· W4239684109 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePsycEXTRA Dataset · 2012
Typedataset
Languageen
FieldEngineering
TopicAdvanced Manufacturing and Logistics Optimization
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTruckOperator (biology)Scheduling (production processes)Computer scienceOperations researchTransport engineeringAutomotive engineeringReal-time computingEngineeringOperations managementBiology

Abstract

fetched live from OpenAlex

This paper presents the findings from a field study of human-automation interaction in an open pit gold mine.Motivated by an earlier study that identified problematic interaction between haul truck operators and dispatch interfaces, focus groups and questionnaires were used to understand what causes the general attitude of suspicion towards the system.Overall trust in the dispatch interface, as well as usability and functionality of the system, were judged as slightly positive.However, the inability of the system to react efficiently to sudden changes on site results in operator frustration.We argue that consequently, the human operator should be utilized as a sensor by the dispatch system.Through operator involvement in the stage of information acquisition, the system's response to sudden changes can be improved, and discontent reduced.

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.

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)0.000
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.284
Threshold uncertainty score0.846

Codex and Gemma teacher scores by category

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

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 head0.022
GPT teacher head0.305
Teacher spread0.283 · 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