MétaCan
Menu
Back to cohort
Record W3163119423 · doi:10.32560/rk.2020.3.4

A leggyakoribb hibák a légi közlekedésben

2020· article· hu· W3163119423 on OpenAlexaffabout
Zoltán Dudas

Bibliographic record

VenueRepüléstudományi Közlemények · 2020
Typearticle
Languagehu
FieldPsychology
TopicHuman-Automation Interaction and Safety
Canadian institutionsTransport Canada
Fundersnot available
KeywordsMedicineFood scienceTraditional medicineChemistry

Abstract

fetched live from OpenAlex

Az emberi hiba kezelésének módja a biztonságkultúra jellegzetessége. A gyakori hibatípusok a repülésbiztonságot bemutató elméleti modellek (Reason; SHEL[L]; SRK) mindegyike számára értelmezhetők. A modellek azonban teljes egészében nem adnak kielégítő választ az emberi hiba természetére. A biztonságfilozófia emberi tényezőt érintő elemeinek feltárására a szerző a Transport Canada tipikus emberi hibákat felsoroló „Piszkos Tizenkettő” koncepcióját mutatja be.

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)0.002
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.040
Threshold uncertainty score0.134

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0020.004
Scholarly communication0.0060.005
Open science0.0010.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0400.009

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 head0.069
GPT teacher head0.372
Teacher spread0.302 · 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

Citations3
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueRepüléstudományi KözleményekSame topicHuman-Automation Interaction and SafetyFrench-language works237,207