Development of a small-scale approach to assess sea ice change using weekly ice charts, with application to Anguniaqvia Niqiqyuam Marine Protected Area
Bibliographic record
Abstract
Broad-scale changes in sea ice have been documented across the Arctic; however, less is known about sea ice decline at smaller scales, focused at high-priority areas such as marine protected areas (MPAs) or places identified as important by Indigenous Peoples. Here, we develop a small-scale application of assessing sea ice change using weekly sea ice charts and apply that to assess sea ice change in Anguniaqvia Niqiqyuam MPA (ANMPA) from 1980 to 2019. Over that 40-year period, sea ice coverage in ANMPA decreased and open water increased by approximately a month (31.6 days at 50% ice; 33.8 days at 20% ice remaining during break-up and 80% ice formed during freeze-up). Break-up has gone from occurring in mid- or late July to occurring in late June or early July. Freeze-up has changed from occurring in mid-October to occurring in early November. As sea ice decline may have dramatic impacts for the ecosystem and consequences for the people that rely on this important area, we highlight the need to better understand the impacts of sea ice decline in small-scale priority places and also contribute to the development of community-scale approaches to increase the accessibility of assessing change.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".