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Record W2325357499 · doi:10.1177/154193120504902308

Establishing Human Performance Improvements and Economic Benefit for a Human-Centered Operator Interface: An Industrial Evaluation

2005· article· en· W2325357499 on OpenAlexaff
Jamie Errington, Dal Vernon C. Reising, Peter Bullemer, Tim DeMaere, Dave Coppard, Keath Doe, Charles P. Bloom

Bibliographic record

VenueProceedings of the Human Factors and Ergonomics Society Annual Meeting · 2005
Typearticle
Languageen
FieldPsychology
TopicHuman-Automation Interaction and Safety
Canadian institutionsNova Chemicals (Canada)
Fundersnot available
KeywordsInterface (matter)Operator (biology)Process (computing)FidelityComputer scienceUpsetMonte Carlo methodHuman interface deviceMatching (statistics)SimulationReliability engineeringIndustrial engineeringHuman–computer interactionEngineeringMechanical engineeringOperating systemTelecommunicationsMathematicsStatistics

Abstract

fetched live from OpenAlex

A controlled comparison of a human-centered operator interface to that of a traditional distributed control system interface was conducted to establish the human performance improvement. Twenty-one professional petrochemical plant operators completed a series of matching process upset scenarios on their respective plants' high-fidelity training simulators. Each scenario contained an equipment or process failure previously experienced in the real plants. The results indicated that operators using the human-centered design completed scenarios an average of 7.5 minutes faster (41% improvement over the traditional interface), successfully dealt with failures in 96% of the cases (a 26% improvement), and recognized the presence of the failure before the first process alarm in 48% of the cases (a 38% improvement). These performance results were then used as input to a Monte Carlo simulation that estimated the economic benefit for the human-centered interface at $1,090,000 CAD per year for a plant of comparable size.

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)0.001
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 score0.106
Threshold uncertainty score0.916

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.059
GPT teacher head0.346
Teacher spread0.287 · 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

Citations25
Published2005
Admission routes1
Has abstractyes

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