First records of winter sea ice concentration in the southwest Pacific sector of the Southern Ocean
Bibliographic record
Abstract
Abstract We use a Generalized Additive Model (GAM) to provide the first winter sea ice concentration record from two cores located within the southwest Pacific sector of the Southern Ocean. To compliment the application of GAM, a time series analysis on satellite records of sea ice concentration data was used to extend the standard 13.25 year time series used for paleoceanography. After comparing GAM sea ice estimates with previously published paleo sea ice data we then focus on a new paleo winter sea ice record for marine sediment core E27‐23 (59°37.1'S, 155°14.3'E), allowing us to provide a more comprehensive view of winter sea ice dynamics for the southwest Pacific Ocean. The paleo winter sea ice concentration estimates provide the first suggestion that winter sea ice within the southwestern Pacific might have expanded during the Antarctic Cold Reversal. Throughout the Holocene, core E27‐23 documents millennial scale variability in paleo winter sea ice coverage within the southwest Pacific. Holocene winter sea ice expansion may have resulted from the Laurentide Ice Sheet deglaciation, increased intensity of the westerly winds, as well as a northern migration of the Subtropical and/or Sub‐Antarctic Fronts. Brief consideration is given to the development of a paleo summer sea ice proxy. We conclude that there is no evidence that summer sea ice ever existed at core sites SO136‐111 and E27‐23 over the last 220 and 52,000 years, respectively.
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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 | 0.001 | 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".