Welfare of aquatic animals: where things are, where they are going, and what it means for research, aquaculture, recreational angling, and commercial fishing
Bibliographic record
Abstract
Abstract We revisit the evidence attributing sentience-pain-suffering to aquatic animals. The objective is to inform readers of the current state of affairs, to direct attention to where research is needed, and to identify “wicked” questions that are difficult to resolve unequivocally. By separating the ethical from the scientific debate, applying organized skepticism to the latter, and taking a pragmatic approach that does not depend on resolving the “wicked” questions, we hope to focus and strengthen research on aquatic animal welfare. A second but closely-related objective is to briefly summarize the research used to support the regulations governing the welfare of aquatic animals, particularly its limitations. If you interact with aquatic animals, these regulations already affect you. If the regulatory environment continues on its current trajectory (adding more aquatic animal taxa to those already regulated), activity in some sectors could be severely restricted, even banned. There are surely some lively debates and tough choices ahead. In the end, extending legal protection to aquatic animals is a societal choice, but that choice should not be ascribed to strong support from a body of research that does not yet exist, and may never exist, and the consequences of making that decision must be carefully weighed.
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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.025 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".