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Record W629038158

Runway risk reduction

2011· article· en· W629038158 on OpenAlexaboutno aff
Linda Werfelman

Bibliographic record

VenueAeroSafety world./AeroSafety world · 2011
Typearticle
Languageen
FieldDecision Sciences
TopicRisk and Safety Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsRunwayAeronauticsAviationCivil aviationAviation engineeringEngineeringAviation safetyService (business)Transport engineeringAviation accidentBusinessMarketing
DOInot available

Abstract

fetched live from OpenAlex

W ith about one-third of all aviation accidents associated with runway operations, the International Civil Aviation Organization (ICAO) has introduced safety initiatives aimed at reducing runway-related accidents. The initiatives were endorsed in late May by ICAO partners within the aviation industry, including Flight Safety Foundation, during the first meeting of the ICAO Global Runway Safety Symposium, held in Montreal.1 “We have a clear understanding on the roles and responsibilities of each of the partners in reducing and working toward eliminating runway incursions and excursions,” Nancy Graham, director of the ICAO Air Navigation Bureau, said. “The multidisciplinary approach is the only option for coming to grips with a complex set of operational and human factors issues.” The initiatives include runway safety seminars to be held around the world to help develop regional action plans and encourage the formation of runway safety teams that will involve airlines, airports and air navigation service providers. Other efforts call for “the compilation and further development of best practices and the greater sharing of information among ICAO member states and industry.” One of the first requirements will be the development of common definitions, metrics and methods of analysis to enable more complete information sharing, as well as the improved reporting of operational hazards. © K en C ol e/ D re am st im e

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)0.009
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.738
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.013
Science and technology studies0.0020.001
Scholarly communication0.0000.002
Open science0.0030.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0120.007

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 head0.125
GPT teacher head0.333
Teacher spread0.208 · 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; both teacher heads agree on what is shown here.

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
Published2011
Admission routes1
Has abstractyes

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