Landfast ice thickness in the Canadian Arctic Archipelago fromobservations and models
Bibliographic record
Abstract
Abstract. Observed and modelled landfast ice thickness variability and trends spanning more than 5 decades within the Canadian Arctic Archipelago (CAA) are summarized. The observed sites (Cambridge Bay, Resolute, Eureka and Alert) represent some of the Arctic's longest records of landfast ice thickness. Observed end-of-winter (maximum) trends of landfast ice thickness (1957–2014) were statistically significant at Cambridge Bay (−4.31 ± 1.4 cm decade−1), Eureka (−4.65 ± 1.7 cm decade−1) and Alert (−4.44 ± 1.6 cm −1) but not at Resolute. Over the 50+-year record, the ice thinned by ∼ 0.24–0.26 m at Cambridge Bay, Eureka and Alert with essentially negligible change at Resolute. Although statistically significant warming in spring and fall was present at all sites, only low correlations between temperature and maximum ice thickness were present; snow depth was found to be more strongly associated with the negative ice thickness trends. Comparison with multi-model simulations from Coupled Model Intercomparison project phase 5 (CMIP5), Ocean Reanalysis Intercomparison (ORA-IP) and Pan-Arctic Ice-Ocean Modeling and Assimilation System (PIOMAS) show that although a subset of current generation models have a "reasonable" climatological representation of landfast ice thickness and distribution within the CAA, trends are unrealistic and far exceed observations by up to 2 orders of magnitude. ORA-IP models were found to have positive correlations between temperature and ice thickness over the CAA, a feature that is inconsistent with both observations and coupled models from CMIP5.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".