Reasons to Be Skeptical about Sentience and Pain in Fishes and Aquatic Invertebrates
Bibliographic record
Abstract
The welfare of fishes and aquatic invertebrates is important, and several jurisdictions have included these taxa under welfare regulation in recent years. Regulation of welfare requires use of scientifically validated welfare criteria. This is why applying Mertonian skepticism toward claims for sentience and pain in fishes and aquatic invertebrates is scientifically sound and prudent, particularly when those claims are used to justify legislation regulating the welfare of these taxa. Enacting welfare legislation for these taxa without strong scientific evidence is a societal and political choice that risks creating scientific and interpretational problems as well as major policy challenges, including the potential to generate significant unintended consequences. In contrast, a more rigorous science-based approach to the welfare of aquatic organisms that is based on verified, validated and measurable endpoints is more likely to result in “win-win” scenarios that minimize the risk of unintended negative impacts for all stakeholders, including fish and aquatic invertebrates. The authors identify as supporters of animal welfare, and emphasize that this issue is not about choosing between welfare and no welfare for fish and aquatic invertebrates, but rather to ensure that important decisions about their welfare are based on scientifically robust evidence. These ten reasons are delivered in the spirit of organized skepticism to orient legislators, decision makers and the scientific community, and alert them to the need to maintain a high scientific evidential bar for any operational welfare indicators used for aquatic animals, particularly those mandated by legislation. Moving forward, maintaining the highest scientific standards is vitally important, in order to protect not only aquatic animal welfare, but also global food security and the welfare of humans.
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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 | 0.038 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.031 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.021 | 0.028 |
| Insufficient payload (model declined to judge) | 0.004 | 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".