Bibliographic record
Abstract
Pacific walruses ( Odobenus rosmarus divergens, Illiger 1815) have long been vital to Indigenous communities along Alaska’s west coast. Although current harvest rates are sustainable, climate change and increased industrial activity in the range of this species pose threats to the population and to hunting safety and success. To gather information relevant to addressing these concerns, the Eskimo Walrus Commission and the US Fish and Wildlife Service held a workshop in August 2023 in Nome, Alaska, with experienced Yupik walrus hunters from the communities of Gambell and Savoonga on St. Lawrence Island, Alaska, and Federal walrus biologists. The 3-day event documented extensive information about walrus biology and behavior, which was used to improve a walrus population model. Workshop discussions also addressed concepts of sustainability and the future of walrus hunting. The workshop benefitted from prior collaboration between the biologists and some of the hunters on a walrus research cruise in the Chukchi Sea earlier the same summer, creating a foundation of common experience and interpersonal relationships. In the longer term, the workshop helped demonstrate the value of equitable collaboration towards shared goals, in part by allowing for open conversations rather than, for example, an extended question-and-answer session regarding model parameters.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".