An assessment of the spatial extent of polar dust using satellite thermal data
Bibliographic record
Abstract
The effect of mineral dust aerosols and subsequent deposition in polar regions has historically been underestimated in climate models. Dust aerosols increase melt rates by reducing surface albedo and modifying atmospheric radiative properties. In this study 127,413 satellite images covering the Arctic and Antarctic from 2007 to 2019 were assessed for dust content using thermal infrared wavelengths. The results show a strong linear trend in which the relative spatial extent of dust (RSED) increased annually by 0.31% in the Arctic (8.5% to 12.1%) and 0.19% in the Antarctic (5.2% to 7.5%). Seasonally, the maximum aggregated average RSED occurred in the Arctic during boreal winter (11.2%), while the Antarctic peaked in austral spring (9.5%). Maximum RSED rates occurred in boreal winter/austral summer (Dec-Jan-Feb) for both polar regions. The data suggests that finer dust particles are more efficiently distributed by aeolian processes leading to higher RSED values that are not necessarily reflective of polar dust loading models.
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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 | 0.001 |
| 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) | 0.000 | 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".