Sea Ice Melt Onset Dynamics in the Northern Canadian Arctic Archipelago from RADARSAT, 1997-2014
Bibliographic record
Abstract
An algorithm was developed to detect melt onset over Arctic sea ice using high-resolution SAR images from RADARSAT. The algorithm is based on the temporal evolution of the SAR backscatter coefficient (σ⁰), using an ice type specific threshold approach that also corrects for backscatter incidence angle variation. Using 4457 RADARSAT images, the algorithm was applied over sea ice in the northern CAA, thus generating a new time series of melt onset from 1997-2014. The mean annual melt onset date was on YD 164±4 (mid–June). No significant trend was found over the 18-year period, however, variability increased in post-2007 years. An earlier (later) melt onset was associated with increased (decreased) solar energy absorption and subsequently associated in lighter (heavier) September sea ice coverage in the northern CAA. RADARSAT estimates of melt onset were found to be in good agreement but more robust compared to passive microwave and scatterometer estimates.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 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".