Bibliographic record
Abstract
Abstract : The long-term goals are: 1) Determination of the net growth and melt of multiyear (MY) sea ice during its transit through the southern Beaufort Sea 2) Identification of key regional processes in southern Beaufort Sea affecting MY ice recruitment 3) Improved predictability of the future states of the Arctic ice pack. We have four main scientific objectives: I) Estimation of MY ice volume entrained into the Beaufort Sea from north of Canada The region north of the Canadian Archipelago contains some of the oldest and thickest ice in the Arctic and the amount of this ice imported into the Beaufort Sea has a significant effect on the overall MY ice budget of the Arctic. II) Estimation of rate of thinning of MY ice during transit through southern Beaufort Sea The thickness of MY ice at the end of its westward transit through the Beaufort Sea will have a critical impact on the volume of MY ice recruited from one year to the next and on navigability in the Beaufort and other marginal seas. III) Assessment of contribution of refreezing of meltwater to overall mass balance of MY ice Meltwater created through surface ablation can refreeze if it finds its way underneath the sea ice where the ocean will typically be at the colder freezing point of seawater. This can create ice lenses and false bottoms beneath the sea ice and make a positive, but poorly-understood, contribution to the mass balance IV) Assessment of the role MY ice dispersal in promoting ice loss We speculate that diminished MY ice in the Beaufort Sea may be a consequence of changes in drift patterns. Moreover, if net drift and divergence increase as MY ice extent decreases, this may represent a feedback process that will accelerate the Arctic s trajectory toward a seasonally ice free state.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".