Review of Arctic sea-ice records over the last millennium from modern, historical, and proxy data sources
Bibliographic record
Abstract
Sea-ice loss and increasing unpredictability have disturbed and harmed Arctic peoples and ecosystems. In addition, studies demonstrate that sea ice plays a key role in climate variability and air–sea CO2 exchanges. Sea-ice data sets provide environmental baselines, validate proxies and models, and serve in regional and temporal comparisons. Accordingly, sea-ice and sea-ice–related variables are particularly valuable in climate modeling, paleoclimatology, and ecology to document past and present environmental changes and predict future outcomes. This article provides an overview of modern, historical, and long-term proxy sea-ice data sets that cover the last millennium. We describe available Arctic sea-ice data sources, discuss each data set’s strengths and limitations, and compare multisourced Arctic sea-ice histories in different regions. We conclude with remarks on the impacts of internal forcing from natural feedbacks and oscillations versus anthropogenic impacts on sea-ice variability. We draw upon remaining uncertainties regarding causes of past sea-ice variability and advocate for continued use of multiple data sources in sea-ice reconstruction–related studies and further development of a multisourced past sea-ice data network.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.021 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".