Quantifying CO<sub>2</sub> Emissions From Smaller Anthropogenic Point Sources Using OCO‐2 Target and OCO‐3 Snapshot Area Mapping Mode Observations
Bibliographic record
Abstract
Abstract We quantify CO 2 emissions from smaller anthropogenic point sources compared with earlier satellite studies, which have mostly focused on mid‐sized (∼10 MtCO 2 /year) and larger fossil fuel burning power plants. Two types of Orbiting Carbon Observatory (OCO) observation modes are used: OCO‐2 Target mode and OCO‐3 Snapshot Area Mapping (SAM) mode. Methods previously used with OCO‐3 SAMs are adapted to quantify CO 2 emissions with OCO‐2 Targets for the first time, demonstrating a similar capability to track emission changes at the Bełchatów Power Station. SAMs and Targets are then applied to quantify emissions from smaller sources in Canada: the Boundary Dam and Poplar River Power Stations in Saskatchewan, and the Suncor and Syncrude Mildred Lake mined oil sands processing facilities in northern Alberta. We verify our method on the nearby Colstrip Power Station in Montana by comparison with hourly reported values. For Canadian sources, only annual emissions are reported, to which our emission estimates cannot be directly compared. Emissions derived from a single satellite overpass correspond to daily or finer temporal scales and thus do not account for source intermittency or variability, which requires multiple revisits to reliably estimate annual emissions. Finally, we average OCO‐3 SAMs on repeated revisits to improve weak enhancement signals above background noise. Averaging SAMs yields mixed results, with improvements achieved only under certain conditions. These studies help to clarify the capabilities and limitations of CO 2 point source emission quantification with current satellites in advance of plans for operational monitoring with future CO 2 satellite missions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".