Model–data comparison and data assimilation of mid-Holocene Arctic sea ice concentration
Bibliographic record
Abstract
Abstract. The consistency between new quantitative reconstructions of Arctic sea ice concentration based on dinocyst assemblages and the results of climate models has been investigated for the mid-Holocene. The response of the models mainly follows the increase in summer insolation, modulated to a limited extent by changes in atmospheric circulation. This leads to differences between regions in the models that are smaller than in the reconstruction. It is, however, impossible to precisely assess the models' skills because the sea ice concentration changes at the mid-Holocene are small in both the reconstructions and the models and of the same order of magnitude as the reconstruction uncertainty. Performing simulations with data assimilation using the model LOVECLIM amplifies the regional differences and improves the model–data agreement as expected. This is mainly achieved through a reduction of the southward winds in the Barents Sea and an increase in the westerly winds in the Canadian Basin, inducing an increase in the ice concentration in the Barents and Chukchi seas. This underlines the potential role of atmospheric circulation in explaining the reconstructed changes during the Holocene.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".