Multiyear ice replenishment in the <scp>C</scp>anadian <scp>A</scp>rctic <scp>A</scp>rchipelago: 1997–2013
Bibliographic record
Abstract
Abstract In the Canadian Arctic Archipelago (CAA), multiyear ice (MYI) replenishment from first‐year ice aging (CAAMYI‐Oct‐1) and Arctic Ocean MYI exchange (CAAMYI‐exchange) contribute to the CAA's relatively heavy sea ice conditions at the end of the summer melt season. We estimate these components using RADARSAT and the Canadian Ice Service Digital Archive and explore processes responsible for interannual variability from 1997 to 2013. CAAMYI‐Oct‐1 (52 ± 36 × 103 km2) provides a larger contribution than CAAMYI‐exchange (13 ± 11 × 103 km2). CAAMYI‐Oct‐1 represents ∼10% of the amount that occurs in the Arctic Ocean. CAAMYI‐exchange represents ∼50% of Nares Strait MYI export to Baffin Bay and ∼12% of Fram Strait MYI export to the Greenland Sea. CAAMYI‐Oct‐1 exhibits dependence on warmer (cooler) summers that increase (decrease) melt evident from strong relationships to surface air temperature (SAT), albedo and total absorbed solar radiation (Qtotal). CAAMYI‐exchange is influenced by summer sea level pressure (SLP) anomalies over the Beaufort Sea and Canadian Basin which shifts the primary source of CAAMYI‐exchange between less obstructed M'Clure Strait (low SLP anomalies) and the more obstructed Queen Elizabeth Islands (high SLP anomalies). Over the 17‐record, appreciable replenishment occurred for most years from 1997 to 2004, reduced replenishment from 2005 to 2012, and large replenishment in 2013. The reduced replenishment period was associated with positive SAT, negative albedo, and positive Qtotal anomalies that facilitated more melt and less CAAMYI‐Oct‐1, together with high SLP anomalies that facilitated less CAAMYI‐exchange. Large replenishment in 2013 was primarily from CAAMYI‐Oct‐1 attributed to strongly negative SAT and Qtotal anomalies and strongly positive albedo that impeded melt.
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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 | 0.001 | 0.002 |
| 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".