Bibliographic record
Abstract
Of all sub-regions of the Canadian Arctic, the Labrador and Baffin Bay display the largest negative trend in summer sea ice area, raising important, questions regarding the length of the “usable-sea-ice season” for coastal communities of Nunatsiavut. In this work, we focus on projected changes in sea ice thickness and extent in the Labrador Sea and Baffin Bay, and more specifically on changes in sea ice season length for four coastal communities, namely Nain, Hopedale, Postville and Rigolet using output diagnostics from the High-Resolution (0.1 degree) Community Earth System Model version 1.3 (CESM1.3-HR) for the period 1850-2100. Given that this high-resolution model does not resolve landfast ice nor the fjord in which the coastal communities are located, the sea ice season length is derived from surface air temperature data (resolved by the model) and a simple freezing degree day model, validated using in-situ, reanalysis and remote sensing data. Results for the Baffin Bay and Labrador Sea show a remarkably stable maximum march sea ice extent in the Labrador Sea followed by a rapid transition to winter ice-free conditions around 2060 when the Arctic Ocean becomes seasonally ice-free and no longer advect thick multi-year sea ice south through the Nares Strait and along the Labrador coastline. This is in contrast with the lower resolution CESM2-LE showing a re-expansion of the maximum sea ice extent starting in the middle of the 21st century followed by a sudden collapse at the end of the century due to a restratification of Labrador Sea and shutdown of deep convection. These results show the importance of resolving small scale process for regional climate projection. Also of interest is the gradual decline in the “usable-sea-ice” season and sporadic extremely large inter-annual variation in sea ice season length in the mid 21st century - a signal that is robust to model spatial resolution and presence or absence of deep convection in the Labrador Sea
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".