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A pilot study of vision-based real-time detection of invisible gas leak and fire

2024· article· en· W4404854193 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

VenueJournal of Physics Conference Series · 2024
Typearticle
Languageen
FieldEngineering
TopicFire Detection and Safety Systems
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsLeakGas leakComputer scienceEnvironmental scienceArtificial intelligenceEngineeringChemistryEnvironmental engineering

Abstract

fetched live from OpenAlex

Abstract The detection of gas leak/fire is a critical element in the safe production, transportation, storage and dispensing of many industrial gases including hydrogen. A novel background detection methodology for IR-insensitive gases was explored in this work. This methodology extracted IR data to a certain target frequency and processed to filter the background signals of the adjacent air and inverse the movements of gas in comparison to the background pixels. Results from the IR data indicate that the background detection methodology is possible and visually outperforms the native FLIR HSM filter. The methodology was capable of capturing both hot-gas and cold-gas flow. Due to the nature of the methodology, detection limits were found to be ΔT∼30 °C for cold-gas helium and ΔT∼20 °C for hot-gas helium, with better ability to detect as the temperature differences increase.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.182
Threshold uncertainty score0.370

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)0.0000.000

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 head0.016
GPT teacher head0.231
Teacher spread0.215 · 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