Arctic sea-ice decline archived by multicentury annual-resolution record from crustose coralline algal proxy
Bibliographic record
Abstract
Northern Hemisphere sea ice has been declining sharply over the past decades and 2012 exhibited the lowest Arctic summer sea-ice cover in historic times. Whereas ongoing changes are closely monitored through satellite observations, we have only limited data of past Arctic sea-ice cover derived from short historical records, indirect terrestrial proxies, and low-resolution marine sediment cores. A multicentury time series from extremely long-lived annual increment-forming crustose coralline algal buildups now provides the first high-resolution in situ marine proxy for sea-ice cover. Growth and Mg/Ca ratios of these Arctic-wide occurring calcified algae are sensitive to changes in both temperature and solar radiation. Growth sharply declines with increasing sea-ice blockage of light from the benthic algal habitat. The 646-y multisite record from the Canadian Arctic indicates that during the Little Ice Age, sea ice was extensive but highly variable on subdecadal time scales and coincided with an expansion of ice-dependent Thule/Labrador Inuit sea mammal hunters in the region. The past 150 y instead have been characterized by sea ice exhibiting multidecadal variability with a long-term decline distinctly steeper than at any time since the 14th century.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".