Comment on egusphere-2025-521
Bibliographic record
Abstract
Abstract. In recent decades, the Arctic climate has changed significantly, especially with a rapid decrease in Arctic Sea ice (ASI) extent in September. This study explores how natural climate variations, specifically linked to the Mainland Indochina Southwest Monsoon (MSWM), affect ASI in September using 40 years of data (1981–2020). The study found that strong MSWM years are associated with less ASI drifting to the Atlantic basin during September, leading to increased sea ice particularly in the Beaufort Sea area. Conversely, weak MSWM years tend to correspond with decreased ASI in certain locations. The MSWM influences the North Atlantic Oscillation (NAO) and North Pacific Oscillation (NPO), altering their typical patterns during strong and weak MSWM years due to interactions between monsoonal heating and the atmosphere-ocean system. During strong MSWM years, a positive NAO and negative NPO weaken the Beaufort Sea High Pressure (BSHP), whereas, during weak MSWM years, the reverse occurs, strengthening the BSHP. And the intensity of the BSHP influences Arctic air-sea interaction, influencing the movement of cold airmass and the track of the transpolar drift stream. This leads to increased sea ice formation during strong MSWM years and decreased formation during weak MSWM years in the Beaufort-Chukchi Sea region.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.016 | 0.008 |
| Insufficient payload (model declined to judge) | 0.231 | 0.133 |
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".