Anthropogenic suffering of farmed animals: the other side of zoonoses
Bibliographic record
Abstract
Wiebers & Feigin’s (W&F’s) target article warns of the zoonotic threat to human health from factory farming and urges phasing out meat production and consumption, for the benefit of both human and nonhuman animals. This commentary focuses on the physical and emotional suffering of farmed animals. This varies by species, production system and geographic location, but suffering is there throughout all stages of production — breeding, housing, transport, usage and slaughter. Ubiquitous monitoring of all facilities where farmed animals are kept, with surveillance cameras recording all phases of production would help reduce some forms of suffering. Other forms are caused by accidents, disease outbreaks and all the “collateral damage” from factory farming. Nor can efforts to improve the welfare of farmed animals be confined to “merely” minimizing their suffering. Their lives need to be made not just bearable but worth living too. It is unrealistic to imagine, however, that all the suffering inflicted on farmed animals by industrial practices and human callousness can be eliminated by efforts to improve their welfare: Welfare measures urgently need to be undertaken and promoted, but they must not be regarded complacently, as if they were a panacea. A panacea would be to phase out animal production, as W&F have proposed, under the imminent zoonotic threat of COVID-19 and its successors.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.002 | 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".