The Continuing Slaughter of Marine Mammals
Bibliographic record
Abstract
The Continuing Slaughter of Marine Mammals Spoiling Japan's Appetite for Whale Meat E nding the Canadian seal hunt remains a major focus of our international efforts to protect marine mammals, but growing pressure on other warm-blooded ocean vertebrates and on marine ecosystems is also a significant concern of Humane Society International (HSI).Despite the global moratorium on commercial whaling, Japan continued to thumb its nose at the humane and conservation communities and at nations that oppose the practice.Under the guise of scientific research, it killed some 1,200 minke, sei, sperm, Bryde's, and fin whales for human consumption and pet food, along with some 20,000 Dall's porpoises and other small cetaceans.We worked closely with the International Whaling Commission and member governments to try and end the Japanese slaughter.Joining with Greenpeace and the Environmental Investigation Agency, we successfully pressured the Japanese company Kyodo Senpaku to end its whaling operations and its parent company Nissui, one of the country's biggest whale meat distributors, to get out of this odious business.We also convinced 7-Eleven in Japan to stop selling whale, dolphin, and porpoise products in its 1,300 stores.Our continued work with Japanese supermarkets has reduced cetacean product sales by at least $6 million.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".