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Record W7037001970

Comparisons of the Orbiting Carbon Observatory-2 (OCO-2) X<sub>CO<sub>2</sub></sub> measurements with TCCON

2017· article· en· W7037001970 on OpenAlexfundno aff

Bibliographic record

VenueMax Planck Digital Library · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicJournalism and Media Studies
Canadian institutionsnot available
FundersEurostarsJet Propulsion LaboratoryUniversité de La RéunionCanadian Space AgencyNova Scotia Research Innovation TrustMax-Planck-GesellschaftCanadian Foundation for Climate and Atmospheric SciencesGovernment of CanadaOntario Innovation TrustNational Aeronautics and Space AdministrationCalifornia Institute of TechnologyU.S. Department of Energy
KeywordsAtmosphere (unit)ResidualCarbon fibersAerosolCarbon dioxideSystematic errorCalibration
DOInot available

Abstract

fetched live from OpenAlex

NASA's Orbiting Carbon Observatory-2 (OCO-2) has been measuring carbon dioxide column-averaged dry air mole fractions, XCO2, in the Earth’s atmosphere for almost two years. In this paper, we describe the comparisons between the OCO-2 version 7Br retrievals and XCO2 estimates from OCO-2's primary ground-based validation network: the Total Carbon Column Observing Network (TCCON). The OCO-2 XCO2 retrievals, after bias correction, agree well globally with the TCCON for nadir, glint, and target observations, with median differences less than 0.5 ppm and RMS differences typically below 1.5 ppm. Target observations over TCCON stations correlate best with the TCCON data (R2 = 0.83) on a global scale. At local scales, the target comparisons reveal residual biases likely related to surface properties and aerosol scattering. It is thus crucial to continue measurement comparisons with TCCON to monitor and evaluate the OCO-2 XCO2 data quality throughout its mission.

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)0.001
metaresearch head score (Gemma)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
Threshold uncertainty score0.155

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.054
GPT teacher head0.258
Teacher spread0.205 · 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
Published2017
Admission routes1
Has abstractyes

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