Integrated pan-Arctic melt onset detection from satellite active and passive microwave measurements, 2000-2009
Bibliographic record
Abstract
[1] An integrated pan-Arctic melt onset data set is generated for the first time by combining estimates derived from active and passive microwave satellite data using algorithms developed for the northern high-latitude land surface, ice caps, large lakes, and sea ice. The data set yields new insights into the spatial and temporal patterns of mean melt onset date (MMOD) and the associated geographic and topographic controls. For example, in the terrestrial Arctic, tree fraction and latitude explain more than 60% of the variance in MMOD, with the former exerting a stronger influence on MMOD than the latter. Elevation is also found to be an important factor controlling MMOD, with most of the Arctic exhibiting significant positive relationships between MMOD and elevation, with a mean value of 24.5 m d−1. Melt onset progresses fastest over land areas of uniform cover or elevation (40–80 km d−1) or both and slows down in mountainous areas, on ice caps, and in the forest-tundra ecotones. Over sea ice, melt onset advances very slowly in the marginal seas, while in the central Arctic the rate of advance can exceed 100 km d−1. Comparison of the observed MMOD with simulated values from the third version of the Canadian Coupled Global Climate Model showed good agreement over land areas but weaker agreement over sea ice, particularly in the central Arctic, where simulated MMOD is about 2–3 weeks later than observed because of a cold bias in simulated surface air temperatures over sea ice.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".