Singular value decomposition analyses of tropical tropospheric ozone determined from TOMS
Bibliographic record
Abstract
A controversial dispute in space‐based tropospheric ozone remote sensing is the puzzling discrepancy in the spatiotemporal distribution between residual‐based satellite ozone observations and biomass‐burning activity in the tropics during boreal winter. This study focuses on evaluation and analyses of two tropospheric ozone products determined from Earth Probe TOMS measurements: Convective Cloud Differential measurements (CCD) and Scan Angle measurements (SAM). Rather than using the typical station‐to‐station inter‐comparison with ozone sounding measurements, the evaluation was performed at the global scale using temporal and spatial patterns derived from Singular Value Decomposition (SVD) analyses. The satellite observations of ozone precursors from MOPITT CO and GOME NO2 serve as markers identifying airmasses influenced by biomass burning. The SVD analyses reveal that the SAM tropospheric ozone product is remarkably consistent (95% significance level) with the two measured ozone precursors, CO and NO2, in distribution as well as in seasonality. The analyses provide compelling evidence that there is no discrepancy between tropospheric ozone and its precursors during boreal winter.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".