Increased Transnational Sea Ice Transport Between Neighboring Arctic States in the 21<sup>st</sup> Century
Bibliographic record
Abstract
Abstract The Arctic is undergoing a rapid transition toward a seasonal ice regime, with widespread implications for the polar ecosystem, human activities, as well as the global climate. Here we focus on how the changing ice cover impacts transborder exchange of sea ice between the exclusive economic zones of the Arctic states. We use the Sea Ice Tracking Utility, which follows ice floes from formation to melt, in conjunction with output diagnostics from two ensembles of the Community Earth System Model that follow different future emissions scenarios. The Community Earth System Model projects that by midcentury, transnational ice exchange will more than triple, with the largest increase in the amount of transnational ice originating from Russia and the Central Arctic. However, long‐distance ice transport pathways are predicted to diminish in favor of ice exchanged between neighboring countries. By the end of the 21st century, we see a large difference between the two future emissions scenarios considered: Consistent nearly ice‐free summers under the high emissions scenario act to reduce the total fraction of transnational ice exchange compared to midcentury, whereas the low emissions scenario continues to see an increase in the proportion of transnational ice. Under both scenarios, transit times are predicted to decrease to less than 2 yr by 2100, compared to a maximum of 6 yr under present‐day conditions and 2.5 yr by midcentury. These significant changes in ice exchange and transit time raise important concerns regarding risks associated with ice‐rafted contaminants.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".