Sea ice and snow phenology in the Canadian Arctic Archipelago from 1997 to 2018
Bibliographic record
Abstract
The multiple islands and narrow channels that form the Canadian Arctic Archipelago (CAA) complicate snow/ice monitoring, as coarse resolution satellite observations are unable to resolve smaller-scale changes in snow/ice cover. We present the first study showing the utility of the Interactive Multisensor Snow and Ice Mapping System (IMS) 24 km (1997–2018) and 4 km (2004–2018) products to investigate changes in sea ice and snow phenology together in the CAA. Although ice break-up and snow retreat are shifting earlier (p > 0.05), on par with other Arctic regions, the final summer clearing of ice is shifting later. This, combined with trends towards earlier ice freeze and snow fall (p < 0.05), result in shorter open water and snow free seasons in the CAA. Spatial links between sea ice and snow are evident as significant clusters of trends were identified for all phenology parameters. The western regions were dominated by shifts towards shorter snow/ice seasons, whereas eastern regions tended to exhibit longer cover. Our research highlights the considerable regional and interannual variability in the timing of sea ice and snow advance/retreat within the CAA and emphasizes how the ice and snow dynamics in this complex region are responding to ongoing changing climate conditions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".