Workshop on Advancing NOAA’s Modeling for Improved Sea Ice Forecasts: Defining Priorities and Key Collaborations
Bibliographic record
Abstract
The NOAA Sea Ice Modeling Collaboration Workshop was held in Boulder, Colorado, on the University of Colorado’s East Campus between 25 and 27 April 2023 amid spring blossoms and a backdrop of the snow-covered Rocky Mountain Continental Divide. Over the 2.5-day workshop, participants shared advancements and challenges in sea and lake ice modeling from NOAA’s Office of Oceanic and Atmospheric Research (OAR) laboratories, other U.S. agencies, NOAA’s cooperative institutes, university partners from Alaska to Maryland and many states in between, and our international colleagues from Canada and Germany. To foster new opportunities for advancing sea ice models through collaboration, presentations, exchanges, and prioritization discussions were focused around our three overriding workshop themes: advancements in coupled (sea ice–wave–ocean–atmosphere–land) modeling development, novel ways of evaluating models, and model applications and transition opportunities.
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 | 0.045 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".