A Pan‐Arctic Network to Study Past, Present, and Future Sea Ice Trends
Bibliographic record
Abstract
Arctic in Rapid Transition Implementation Workshop; Winnipeg, Manitoba, Canada, 18–20 October 2010; Rapid transitions in Arctic sea ice and the associated global integrated Earth system impacts and socioeconomic consequences have brought the Arctic Ocean to the top of national and international geophysical and political agendas. Alarmingly, there is a persistent mismatch between observed and predicted patterns, which speaks to the complexity of planning adaptation and mitigation activities in the Arctic. Predicting future conditions of Arctic marine ecosystems for climate change requires interdisciplinary and pan‐Arctic characterization and understanding of past and present trends. The Arctic in Rapid Transition (ART) initiative is an integrative, international, interdisciplinary, pan‐Arctic network to study spatial and temporal changes in sea ice cover and ocean circulation over broad time scales to better understand and forecast the impact of these changes on Arctic marine ecosystems and biogeochemistry. The ART initiative began in October 2008 and is still led by early‐career scientists. The ART science plan, developed after the ART initiation workshop in November 2009, was endorsed by the Arctic Ocean Sciences Board, which is now the Marine Working Group of the International Arctic Science Committee.
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.008 | 0.002 |
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".