Changing sea ice melt parameters in the Canadian Arctic Archipelago: Implications for the future presence of multiyear ice
Bibliographic record
Abstract
Estimates of annual sea ice melt onset, freeze onset, and melt duration are made within the Canadian Arctic Archipelago (CAA) using SeaWinds/QuikSCAT data from 2000 to 2007. The average date of melt onset occurred on day 150, the average freeze onset occurred on day 266, and the average number of days of melt was 116. Melt onset occurred first, and freeze onset occurred last within the Amundsen, Western Arctic Waterway, and Eastern Parry Channel regions, whereas the reverse occurred in the Queen Elizabeth Islands (QEI) and the M'Clure and Viscount‐Melville regions. Multiyear sea ice (MYI) increases occurred from 2000 to 2004 because of dynamic import and first‐year sea ice (FYI) being promoted to MYI, but this replenishment virtually stopped from 2005 to 2007, coincident with longer melt seasons. Only after two consecutive long melt seasons (2005–2006) and almost no replenishment were regions to the south of the QEI cleared of MYI. We argue that this is because MYI must slowly ablate on the underside while in transit within the CAA from the small oceanic heat flux and can therefore survive for several years in southern regions without replenishment. Net positive dynamic MYI import into the CAA was observed in 2007 following MYI removal during 2005–2006. Longer melt seasons will continue to reduce the inventory of FYI in the CAA following the melt season. Longer melt seasons within the CAA will likely not reduce MYI dynamic import, but it remains to be seen whether or not this MYI will be able to survive longer melt seasons as it migrates to the southern regions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".