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Record W3039476737 · doi:10.3390/inventions5030028

A Review of Methane Gas Detection Sensors: Recent Developments and Future Perspectives

2020· review· en· W3039476737 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueInventions · 2020
Typereview
Languageen
FieldEngineering
TopicGas Sensing Nanomaterials and Sensors
Canadian institutionsUniversity of Waterloo
FundersUniversity of WaterlooNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsSaudi Arabian Cultural BureauUniversity of Hafr Al Batin
KeywordsMethaneGreenhouse gasEnvironmental scienceNatural gasProcess engineeringComputer scienceEngineeringWaste managementChemistry

Abstract

fetched live from OpenAlex

Methane, the primary component of natural gas, is a significant contributor to global warming and climate change. It is a harmful greenhouse gas with an impact 28 times greater than carbon dioxide over a 100-year period. Preventing methane leakage from transmission pipelines and other oil and gas production activities is a possible solution to reduce methane emissions. In order to detect and resolve methane leaks, reliable and cost-effective sensors need to be researched and developed. This paper provides a comprehensive review of different types of methane detection sensors, including optical sensors, calorimetric sensors, pyroelectric sensors, semiconducting oxide sensors, and electrochemical sensors. The discussed material includes the definitions, mechanisms and recent developments of these sensors. A comparison between different methods, highlighting the advantages and disadvantages of each, is also presented to help address future research needs.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.956
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.036
GPT teacher head0.283
Teacher spread0.248 · 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