MétaCan
Menu
Back to cohort

Managing the Unexpected

2011· book· en· W232366232 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

VenueOxford University Press eBooks · 2011
Typebook
Languageen
FieldDecision Sciences
TopicSimulation Techniques and Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCrewChecklistExploratory researchComputer scienceOperations researchRisk analysis (engineering)SimulationPsychologyEngineeringAeronauticsCognitive psychologyBusinessSociology

Abstract

fetched live from OpenAlex

How to react to an unexpected and challenging situation that has never been thought of before it happened? A situation for that no checklist exists and no training could be performed? The EU-funded project Man4Gen, Manual Operations of 4th Generation Airliners, which has been successfully completed in 2016, employed human-in-the-loop simulation as an e�ective tool used to analyze the crew response in unexpected and ambiguous situations. Based on an exploratory simulator study on the crews' behavior during these situations the so-called Risk Information System was developed to support crews in their decision-making and problem solving. The paper gives an overview of the conduction and results of the exploratory simulator study leading to the development of the Risk Information System. The system's new philosophy of displaying failures is explained and the results of its proof-of-concept evaluation are shown.

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: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.800
Threshold uncertainty score0.599

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 science0.0020.001
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 head0.119
GPT teacher head0.309
Teacher spread0.190 · 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