Extreme low sea ice years in the Canadian Arctic Archipelago: 1998 versus 2007
Bibliographic record
Abstract
Extreme sea ice minima were observed within the Canadian Arctic Archipelago (CAA) during 1998 and 2007. The September average sea ice area was 2.90 and 2.65 standardized anomalies below the historical 1968–1996 climatology for 1998 and 2007, respectively. October sea ice area for 1998 was a staggering 4.45 standardized anomalies below the historical 1968–1996 climatology and 2007 was lower by 3.36 standardized anomalies. We examine the role of thermodynamic and dynamic forcing on CAA sea ice that was responsible for its extreme loss in 1998 and 2007. Thermodynamic forcing on the sea ice was concentrated over 1 month in 2007 facilitating rapid melt, contrasted against a long melt season in 1998. This variation was attributed to anomalously warm air temperatures in June, September, and October for 1998 compared to anomalously warm temperatures in July for 2007. Sea ice dynamics contributed to the 1998 minimum by inhibiting replenishment from the Arctic Ocean but actually facilitated replenishment in 2007 thereby preventing record low conditions. Replenishment was driven by dissimilarities in sea level pressure patterns over the CAA during these extreme years. Evidence for preconditioned thinning was apparent leading up to 2007 but not strongly apparent for 1998. Remarkably, at the onset of 1998 melt season, multi‐year ice area within the CAA was 11% more than the historical climatology and 48% more than at the start of the 2007 melt season yet an extreme minima was still reached.
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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 | 0.003 |
| 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".