EOS, Transactions, American Geophysical Union Natural Variability of Arctic Sea Ice Over the Holocene
Bibliographic record
Abstract
The area and volume of sea ice in the Arctic Ocean is decreasing, with some predicting ice-free summers by 2100 A.D. [Johannessen et al., 2004]. The implications of these trends for transportation and ecosystems are profound; for example, summer shipping through the Northwest Passage could be possible, while loss of sea ice could cause stress for polar bears. Moreover, global climate may be affected through albedo feedbacks and increased sea ice production and export. With more open water, more new sea ice forms in winter, which melts and/or gets exported out of the Arctic. The recent decrease in summer sea ice (Figure 1a) may result from radiative forcing, possibly due to increased greenhouse gas concentrations, and/or from reduced winter ice cover which allows greater atmospheric warming [Rigor et al., 2002]. While several studies predict a continuous decline in ice cover, the timing, magnitude, and regional expression vary between models [e.g., Johannessen et al., 2004]. For example, the Canadian Arctic Archipelago (CAA) may remain encumbered with summer ice, because multi-year ice accumulates along its coastline and invades the channels [Agnew et al., 2001]. Interestingly, the Holocene sea ice history of the CAA indicates less summer sea ice 10,500–9000 years before present (B.P.), perhaps similar to current trends. All sea ice proxies point to an early Holocene ice cover minimum, but regional differences characterize later times. A consortium of Canadian groups is using ocean cores, ice cores, and mammalian and archeological histories to build a Holocene sea ice history; preliminary results are reported here. By the end of International Polar Year activities in 2008, more will be known about the natural variability of sea ice during past times. Although sea level changed over the Holocene, tracing sea ice history across the
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.012 |
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".