Natural variability of the Arctic Ocean sea ice during the present interglacial
Bibliographic record
Abstract
The impact of the ongoing anthropogenic warming on the Arctic Ocean sea ice is ascertained and closely monitored. However, its long-term fate remains an open question as its natural variability on centennial to millennial timescales is not well documented. Here, we use marine sedimentary records to reconstruct Arctic sea-ice fluctuations. Cores collected along the Lomonosov Ridge that extends across the Arctic Ocean from northern Greenland to the Laptev Sea were radiocarbon dated and analyzed for their micropaleontological and palynological contents, both bearing information on the past sea-ice cover. Results demonstrate that multiyear pack ice remained a robust feature of the western and central Lomonosov Ridge and that perennial sea ice remained present throughout the present interglacial, even during the climate optimum of the middle Holocene that globally peaked ∼6,500 y ago. In contradistinction, the southeastern Lomonosov Ridge area experienced seasonally sea-ice-free conditions, at least, sporadically, until about 4,000 y ago. They were marked by relatively high phytoplanktonic productivity and organic carbon fluxes at the seafloor resulting in low biogenic carbonate preservation. These results point to contrasted west-east surface ocean conditions in the Arctic Ocean, not unlike those of the Arctic dipole linked to the recent loss of Arctic sea ice. Hence, our data suggest that seasonally ice-free conditions in the southeastern Arctic Ocean with a dominant Arctic dipolar pattern, may be a recurrent feature under "warm world" climate.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".