Report of the 2024 snow crab workshop: Clawing their way back; A comparative Newfoundland–Alaska snow crab workshop toward sustainable management in uncertain times
Bibliographic record
Abstract
An international workshop on snow crabs (Chionoecetes opilio) was convened from 29 April through 2 May 2024, in St. John’s, Newfoundland, Canada. It was jointly organized by Fisheries and Oceans Canada and the Bering Sea Fisheries Research Foundation (based in Seattle, Washington, USA) and was hosted by the Fisheries and Marine Institute of the Memorial University of Newfoundland. The workshop was convened to allow researchers and industry partners from Atlantic and Pacific regions to share information regarding the dynamics of their stocks, approaches to management, and plan for the future. A catalyst for the workshop was the collapse of the eastern Bering Sea (EBS) snow crab stock during 2018–2021, which caused an economic disaster for fishery stakeholders and fishing communities across the Pacific Northwest and Alaska. US Disaster relief funding is available to support fishers in Alaska, and a portion of these funds are available to fund research. The development of research recommendations formed an important component of the discussions.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 0.005 |
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".