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Record W2982239427 · doi:10.1029/2019gl083798

Satellite Discovery of Anomalously Large Methane Point Sources From Oil/Gas Production

2019· article· en· W2982239427 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

VenueGeophysical Research Letters · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsGHGSat (Canada)
FundersEarth Sciences DivisionStichting voor de Technische WetenschappenMinistry of Economic Affairs
KeywordsMethaneTonneEnvironmental scienceSatelliteCompressor stationAtmospheric methaneNatural gasAtmosphere (unit)Atmospheric sciencesMethane gasMeteorologyRemote sensingGeologyPhysicsGeographyChemistry

Abstract

fetched live from OpenAlex

Abstract Rapid identification of anomalous methane sources in oil/gas fields could enable corrective action to fight climate change. The GHGSat‐D satellite instrument measuring atmospheric methane with 50‐meter spatial resolution was launched in 2016 to demonstrate space‐based monitoring of methane point sources. Here we report the GHGSat‐D discovery of an anomalously large, persistent methane source (10–43 metric tons per hour, detected in over 50% of observations) at a gas compressor station in Central Asia, together with additional sources (4–32 metric tons per hour) nearby. The TROPOMI satellite instrument confirms the magnitude of these large emissions going back to at least November 2017. We estimate that these sources released 142 ± 34 metric kilotons of methane to the atmosphere from February 2018 through January 2019, comparable to the 4‐month total emission from the well‐documented Aliso Canyon blowout.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.814
Threshold uncertainty score0.999

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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 head0.011
GPT teacher head0.244
Teacher spread0.234 · 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