Update on the status of gray whales since the 2020 Implementation Review
Bibliographic record
Abstract
The International Whaling Commission’s Scientific Committee conducts regular Implementation Reviews (IRs) of the biology, threats and status of whale species subject to aboriginal subsistence whaling. The last IR of plans for hunting eastern North Pacific (ENP) gray whales by the Chukotka Natives of the Russian Federation and the Makah Tribe of the United States of America occurred in 2020. This paper presents a review of new scientific findings on gray whales to assess whether the current status of the stock(s) is within the parameter space tested in the 2020 IR. Updated information on gray whale stock structure hypotheses, abundance and calf productivity, health and strandings, human removals by hunting and non‐hunting sources, population growth rates, immigration into the Pacific Coast Feeding Group, parameterisation of the Makah hunt, and future episodic mortality events (EMEs) were reviewed for this assessment. For almost all factors, it appears that the current dynamics of the ENP gray whale population are within the parameter space evaluated in 2020 IR. The exception is that EMEs affecting whales in the ENP are occurring more frequently and at a greater magnitude than previously evaluated. However, preliminary evaluations suggest that the performances of the Gray Whale Strike Limit Algorithm (SLA) and Makah Management Plan are robust to recent and future EMEs of Northern Feeding Group gray whales and reductions of productivity of the Pacific Coast Feeding Group, at least under the initial parameterisations. We therefore conclude that there is no compelling need for a Special IR prior to the next scheduled IR in 2026, while noting that additional abundance data for 2022/23 and 2023/24 analysed after drafting this paper could strengthen or weaken the evidence for this conclusion.
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 | 0.028 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.013 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".