Recent changes in pan‐Arctic melt onset from satellite passive microwave measurements
Bibliographic record
Abstract
A new satellite passive microwave (PMW) melt onset retrieval algorithm based on temporal variations in the differences of the brightness temperature between 19 and 37 GHz is shown to be as effective as radar (e.g., QuikScat) measurements. The PMW technique shows improved melt estimates that are more closely linked to observed snow‐off dates than previous studies. An integrated pan‐Arctic (north of 50°N) melt onset date (MOD) dataset is produced by combining estimates on land and sea ice for the entire satellite PMW record. During the 1979–2011 period, significant trends of 2~3 days (decade) −1 to earlier MOD are mainly concentrated over the Eurasian land sector of the Arctic, consistent with changes in spring snow cover extent observed with visible satellite data. The variability and change in melt onset are largely driven by spring surface air temperature, with insignificant influence from low‐frequency modes of atmospheric circulation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 0.002 |
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; both teacher heads agree on what is shown here.
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".