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Record W4389508803 · doi:10.1177/21695067231192443

Designing, evaluating, and benchmarking auditory alarms for the chemical and oil processing industry

2023· article· en· W4389508803 on OpenAlexaff
Judy Edworthy, Deborah Reed, Connor Wessel, Liam Lawrence

Bibliographic record

VenueProceedings of the Human Factors and Ergonomics Society Annual Meeting · 2023
Typearticle
Languageen
FieldMedicine
TopicHealthcare Technology and Patient Monitoring
Canadian institutionsMcMaster University
Fundersnot available
KeywordsBenchmarkingControl (management)Computer scienceAviationPetroleum industryBest practiceEngineeringArtificial intelligenceBusiness

Abstract

fetched live from OpenAlex

The auditory alarms used in oil and chemical processing control rooms are often based on practices and knowledge which is twenty to thirty years out of date, and therefore do not embody the significant progress that has been more recently made in this area. Best practice available from other areas (aviation, transport, and healthcare) can be brought to bear in improving and updating control room alarms as they share similar problems with alarms in those domains. This paper describes the processes of designing, benchmarking, and testing a series of sets of auditory alarms intended for use in control rooms by the oil and chemical processing industry. In particular, the work shows how important the localizability of alarms can be in practice, and how improved localizability can be designed into auditory alarms.

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 score0.231
Threshold uncertainty score0.722

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.054
GPT teacher head0.326
Teacher spread0.272 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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