Contributions of growth and deformation to monthly variability in sea ice thickness north of the coasts of Greenland and the Canadian Arctic Archipelago
Bibliographic record
Abstract
Abstract Regional variability in monthly CryoSat‐2 sea ice thickness is partitioned into contributions from dynamics and thermodynamics using ice deformation calculated from large‐scale ice drift. For five winters (December to April, 2011–2015), over a region of persistent convergence north of the coasts of Greenland and the Canadian Arctic Archipelago, deformation explains ~34% of the overall variance (up to 69% in 2014/2015) in monthly thickness changes. Approximately 42–56% (or ~ 0.6 m) of the seasonal changes in mean regional ice thickness can be attributed to divergence and shear. The estimated area‐averaged growth of 0.12 ± 0.03 m/month compares favorably with measurements from ice mass balance buoys. Examination of the time‐variable thickness distributions shows areas covered by ice < 3 m are reduced, while areas of thicker ice (>3 m) increased. Albeit at fairly coarse resolution, this coupled analysis of thickness changes and deformation offered a first look at the character of the regional thickness redistribution process.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".