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Record W599175047

ARFF international training standards: one goal, several agencies

2013· article· en· W599175047 on OpenAlexaboutno aff
David Y Whitaker

Bibliographic record

VenueAirport Magazine · 2013
Typearticle
Languageen
FieldEngineering
TopicFire dynamics and safety research
Canadian institutionsnot available
Fundersnot available
KeywordsCivil aviationAeronauticsAgency (philosophy)AviationFire protectionEngineeringTraining (meteorology)FirefightingAdministration (probate law)Aviation engineeringService (business)Aviation safetyTransport engineeringInternational airportBusinessPolitical scienceCivil engineeringLawGeographyMarketing
DOInot available

Abstract

fetched live from OpenAlex

The majority of the world's airports are represented by five regulatory authorities: the International Civil Aviation Organization (ICAO), the Federal Aviation Administration (FAA), the European Aviation Safety Agency (EASA), The Air Accident Investigation Branch (as part of the United Kingdom's department of transportation, as well as its Civil Aviation Authority, and Transport Canada. Two international Aircraft Rescue and Fire Fighting (ARFF) publications have become the global standard, as follows: the National Fire Protection Association (NFPA)'s 1003 Standard for Airport Fire Fighter Professional Qualifications, as well as NFPA 402, 403, 405, 412, 414, 415 and 424, which can all be referenced for airport use; and the International Fire Service Training Association (IFSTA) training manuals, written by fire fighters for fire fighters.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.681
Threshold uncertainty score0.998

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.018
GPT teacher head0.236
Teacher spread0.218 · 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.

Study designSimulation or modeling
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
Published2013
Admission routes1
Has abstractyes

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