MétaCan
Menu
← Back to cohort
Record W4406002965 · doi:10.26434/chemrxiv-2025-5ff89

Performance of survey solutions under single-blind controlled testing protocol.

2025· preprint· en· W4406002965 on OpenAlexaboutno aff
Chiemezie Ilonze, Rachel Day, Ethan Emerson, Aidan Duggan, Ryan Brouwer, Daniel Zimmerle

Bibliographic record

VenueChemRxiv · 2025
Typepreprint
Languageen
FieldEngineering
TopicIoT and GPS-based Vehicle Safety Systems
Canadian institutionsnot available
FundersU.S. Department of Energy
KeywordsProtocol (science)Computer scienceMedicine

Abstract

fetched live from OpenAlex

Standardized controlled testing of advanced methane detection technologies (solutions) has been identified as a step in demonstrating the emissions mitigation equivalence between these solutions and existing regulatory-approved leak detection and repair methods (e.g., ground-based optical gas imaging [OGI] camera survey) in the US and Canada. In this study, 12 solutions consisting of 4 handheld OGI cameras, 4 advanced handheld systems, and 4 mobile (automobile- and drone-based) solutions were tested under a single-blind controlled testing protocol at different periods between 2021 and 2023 at an outdoor test facility that simulates emissions from a simple, onshore North American production oil and gas (O&G) facility. Three solutions were tested again 3–12 months after the first test, using the same test protocol and facility to assess how performance changed over time. Results showed that handheld OGI cameras had comparable or better performance in terms of lower 90% probability of detection (DL90), false positive and negative fractions, and higher equipment unit-level localization performance compared to other categories of solutions tested. Advanced handheld solutions had comparable performance with the OGI cameras across all metrics except false positive fraction (much higher), while mobile solutions generally had shorter survey durations compared to other solutions. For solutions that tested twice, the performance of 2 of 3 solutions generally improved, illustrating the benefit of regular, comprehensive testing in the development of solutions. The different value propositions inferred from the various categories of solutions tested suggest that mobile solutions can rapidly survey larger areas to inform more targeted, follow-up inspections with handheld solutions. While advanced handheld solutions can be as effective as OGI cameras, mobile solutions have some difficulties to overcome to make them directly comparable or need to have a different use case.

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)0.006
metaresearch head score (Gemma)0.010
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: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.010
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.098
GPT teacher head0.280
Teacher spread0.182 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueChemRxiv→Same topicIoT and GPS-based Vehicle Safety Systems→French-language works237,207→