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Record W2117605914 · doi:10.1029/2002jd003186

Operational carbon monoxide retrieval algorithm and selected results for the MOPITT instrument

2003· article· en· W2117605914 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 Geophysical Research Atmospheres · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsUniversity of Toronto
FundersInstitut national des sciences de l'UniversCentre National de la Recherche ScientifiqueNational Aeronautics and Space Administration
KeywordsA priori and a posterioriTroposphereRemote sensingEnvironmental scienceContext (archaeology)SatelliteWeightingMeteorologyCarbon monoxideAlgorithmComputer scienceGeologyGeographyChemistry

Abstract

fetched live from OpenAlex

Measurements of Pollution in the Troposphere (MOPITT) is a new remote sensing instrument aboard the Earth Observing System (EOS) “Terra” satellite which exploits gas correlation radiometry principles to quantify tropospheric concentrations of carbon monoxide (CO) and methane (CH 4 ). The MOPITT CO retrieval algorithm employs a nonlinear optimal estimation method to iteratively solve for the CO profile which is statistically most consistent with both the satellite‐measured radiances and a priori information. The algorithm's theoretical basis is described in terms of the observed radiances and their weighting functions, the a priori information, and the retrieval averaging kernels. Examples of actual CO retrievals over scenes with contrasting pollution conditions are demonstrated, and interpreted in the context of the retrieval averaging kernels and a priori.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.597
Threshold uncertainty score0.392

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0000.000
Research integrity0.0000.001
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 head0.018
GPT teacher head0.273
Teacher spread0.254 · 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