Variations in land types detected using methane retrieved from space-borne sensor
Bibliographic record
Abstract
Abstract. Methane (CH4), a potent greenhouse gas, traps heat in the atmosphere and significantly contributes to global warming. Atmospheric CH4 comes from various natural and anthropogenic sources. CH4 emissions from the decomposition of organic material by bacteria in natural wetlands, other land types, agriculture, and waste management constitute the major component of global emissions. Although there is no clear evidence that CH4 emissions from wetlands and other natural sources have increased substantially in the last decade, uncertainties remain regarding sources and their spatial extent causing discrepancies between emission estimates from inventories/models and estimates inferred by an ensemble of atmospheric inversions. Here we show that satellite-based CH4 total column measurements along with surface albedo from Sentinel-5 Precursor (S-5p) show unique sensitivity to certain land types. Consequently, the areal extent of six land types (marsh, swamp, forest, grassland, cropland, and barren-land) could be identified with high overall accuracy by analysing S-5p data over Canada utilising our classification-segmentation algorithm. Monthly and yearly inventory maps were created, which can be used to validate or complement global models where data from other sources are missing and may help in further constraining the methane budget.
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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".