From Pigs to Silkworms: Cognition and Welfare across 10 Farmed Taxa
Bibliographic record
Abstract
Abstract Billions of animals across many taxa are extensively farmed, with critical impacts on animal welfare. Societal efforts to reduce animal suffering lack rigorous and systematic approaches that facilitate maximising welfare improvements, such as informed funding allocation decisions. We present a multi-measure, cross-taxa framework for modelling differences in pain, suffering, and related cognition to assess whether certain animals have larger welfare ranges (how well or badly animals can fare). Measures include behavioural flexibility, cognitive sophistication, and general learning. We evaluated 90 empirically detectable proxies for cognition and welfare range (henceforth ‘proxies’) in pigs, chickens, carp, salmon, octopus, shrimp, crabs, crayfish, bees, and silkworms. We grouped a subset of proxies into: A) 10 ideal proxies and B) 10 less ideal proxies but with sufficient data for interspecies comparisons. We graded the strength of evidence per proxy across taxa, and constructed a cognition and welfare range profile, with overall judgement scores (ranging from likely no/low confidence to yes/very high confidence). We discuss the implications of comparisons and highlight key avenues for future research. This work is timely, given recent indications of significant political will towards reducing animal suffering, such as the inclusion of cephalopods and decapods in the Animal Welfare (Sentience) Bill following a UK government-commissioned research review. Given the novelty and robustness of our review, we believe it sets a new standard for investigating interspecies comparisons of cognition and welfare ranges and helps inform future research. This should help streamline funding allocations and improve the welfare of millions of farmed animals. Graphical/ Visual Abstract and Caption Cognition and welfare in farmed animals - from pigs to silkworms (Free stock images: http://www.pixabay.com )
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".