Arctic marginal ice zone interannual variability and change point detection using two definitions (1983–2022)
Bibliographic record
Abstract
Abstract The ongoing decline in Arctic sea ice extent and thickness underscores the scientific significance of monitoring the marginal ice zone (MIZ), a transitional region between the open ocean and pack ice. In this study, we used Bootstrap sea ice concentration (SIC) to detect the trend and change point of the Arctic MIZ over 40 years (1983–2022) using two different MIZ definitions: SIC threshold-based (MIZ t ) and SIC anomaly-based (MIZ σ ). This study marks the exploration of a SIC anomaly-based definition of the MIZ over the Arctic. While the two MIZ definitions yield comparable seasonal trends in marginal ice zone fraction (MIZF), the MIZ σ fraction values peak during the transition periods (e.g. freeze-up and break-up), while the MIZ t fraction values peak in August. The analysis also uncovers consistently higher MIZF values for the MIZ σ than for MIZ t across all seasons. Moreover, October and August show the fastest rate of increase in MIZ t fraction and MIZ σ fraction, reflecting the coinciding rapid decrease in sea ice extent during those particular months. Employing the pruned exact linear time, a multiple change point detection method, highlights a significant increase in the MIZ t fraction in October (after 2005) and MIZ σ fraction in August (after 2007). This can be indicative of the recent climate change impacts in the Arctic region that may be linked with shifts in SIC and sea ice mobility for MIZ t and MIZ σ , 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".