Examining the Transition from a perennial to a seasonal sea ice cover in the Arctic Ocean: A Lagrangian Approach
Bibliographic record
Abstract
Background: Declining Arctic sea ice extent has been accompanied by a large loss in multiyear ice (MYI). The dynamic and thermodynamic processes which affect this transition include promotion of first year ice (FYI) to MYI, demotion (melting) of MYI to open water, and ice export through Fram Strait. In this study we quantify the relative importance of these three processes. Methods: We use the Lagrangian Ice Tracking System which employs satellite-derived sea ice drift vectors combined with sea ice concentrations to find annual areas of promotion, demotion, and export. Results: Over the satellite record (1989-2015), we quantify the total contributions to sea ice extent loss from promotion (+30 million km2), demotion (-19.7 million km2), and export of MYI (-18.6 million km2). The result is a total net loss of 8.3 million km2 of MYI. We find that all three processes are positively correlated with minimum sea ice extent and are increasing with rates of +0.165 million km2/decade, -0.146 million km2/ decade, and -0.096 million km2/decade for promotion, demotion, and export respectively. We also compute the negative ice growth feedback at 0.59 (with r2=0.27). This indicates that ice pack recovers, on average, 59% of the MYI area lost to demotion/export through promotion of FYI the following winter. Limitations: Uncertainties in the drift speed are compounded by the weekly temporal resolution of the model, which affects the resulting estimates of demotion and promotion area. Conclusion: Demotion and export combined are increasing faster than promotion and represent a larger area contribution. This imbalance accounts for the observed loss of MYI area.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".