MétaCan
Menu
Back to cohort
Record W1841757638 · doi:10.14429/dsj.1.3302

General features of aircraft instruments

2013· article· en· W1841757638 on OpenAlexaff
Satgursaran Singh

Bibliographic record

VenueDefence Science Journal · 2013
Typearticle
Languageen
FieldEngineering
TopicAerospace and Aviation Technology
Canadian institutionsAir Canada
Fundersnot available
KeywordsAerospace engineeringEnvironmental scienceMeteorologyAeronauticsHumidityAtmospheric sciencesRemote sensingEngineeringGeographyPhysics

Abstract

fetched live from OpenAlex

The aircraft instruments distinguish themselves from those used in other spheres of industry by their ability to operate under more severe conditions of service. For instance when an aircraft is on the tarmac on a hot summer day in the tropics the temperature in the vicinity of the aircraft may be anything upto120°F. As soon as it takes off and gains height temperature starts decreasing rapidly until at height of 4000ft., it may be as low as -20 ˚F. Similarly on a long distance cross-country flight the aircraft may have to pass through many zones of high and low temperatures. Not only that, the humidity may also vary as rapidly.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.041
Threshold uncertainty score0.136

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0410.035

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 head0.006
GPT teacher head0.214
Teacher spread0.209 · 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 designNot applicable
Domainnot available
GenreOther

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

Explore more

Same venueDefence Science JournalSame topicAerospace and Aviation TechnologyFrench-language works237,207