Application of a SeaWinds/QuikSCAT sea ice melt algorithm for assessing melt dynamics in the Canadian Arctic Archipelago
Bibliographic record
Abstract
A remotely sensed sea ice melt algorithm utilizing SeaWinds/QuikSCAT (QuikSCAT) data is developed and applied to sea ice the Canadian Arctic Archipelago (CAA) from 2000 to 2004. The extended advanced very high resolution radiometer Polar Pathfinder (APP‐x) data set is used to identify spatially coupled relationships between sea ice melt and radiative forcings. In situ data from the Collaborative Interdisciplinary Cryospheric Experiment (C‐ICE) (2000, 2001, and 2002) and the Canadian Arctic Shelf Exchange Study (CASES) (2004) are used to validate APP‐x data during the melt period. QuikSCAT‐detected maps of melt onset, pond onset, and drainage are created from 2000 to 2004, and results indicate considerable interannual variability of melt dynamics within the CAA. In some years, melt stages are positively spatially autocorrelated, whereas other years exhibit a negative or no spatial autocorrelation. QuikSCAT‐detected stages of melt are found to be influenced by interannual varying amounts and timing of radiative forcing making prediction difficult. The spatiotemporal variability of ice melt also influences the distribution of ice within the CAA. The lower‐latitude regions of the CAA are shown to have accumulated increasing concentrations of multiyear ice from 2000 to 2005. This paper concludes with a discussion of the interplay between thermodynamic and dynamic sea ice processes likely to have contributed to this trend.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".