MétaCan
Menu
Back to cohort
Record W2082029565 · doi:10.1177/1541931213571311

The Impact of Sudden Events and Spatial Configuration on the Benefits of Prior Information to Situation Awareness and Performance

2013· article· en· W2082029565 on OpenAlexaff
Avi Parush, Nadya Rustanjaja

Bibliographic record

VenueProceedings of the Human Factors and Ergonomics Society Annual Meeting · 2013
Typearticle
Languageen
FieldPsychology
TopicHuman-Automation Interaction and Safety
Canadian institutionsCarleton University
Fundersnot available
KeywordsFirefightingTask (project management)Event (particle physics)ApartmentComputer scienceSimulationEngineeringGeographyCartographyCivil engineering

Abstract

fetched live from OpenAlex

Having task-relevant information prior to performing the task can improve performance and situation awareness. This study examined the impact of spatial complexity (apartment vs. office buildings) and the presence of sudden events on the benefits of having information prior to fighting fire in a burning building. Performance of an urban firefighting team (finding victims) and situation awareness (SA) were measured in a lab simulation study. Findings show that having complete prior information did improve SA, performance was better in the office building, and performance deteriorated after a sudden event. However, an in-depth examination of the findings suggests that characteristics of the spatial configuration and the presence of sudden events influence the benefits of having prior information.

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)0.009
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.020
GPT teacher head0.287
Teacher spread0.268 · 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

Citations5
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the Human Factors and Ergonomics Society Annual MeetingSame topicHuman-Automation Interaction and SafetyFrench-language works237,207