“We Always Hurt the Things We Love”—Unnoticed Abuse of Companion Animals
Bibliographic record
Abstract
Despite the fact that companion animals enjoy the status of "members of the family" in contemporary society, there are numerous diseases affecting the longevity of these animals and their quality of life. Some of the most pervasive and damaging problems accrue to pedigreed animals whose genetic lines contain many major and severe diseases which are detrimental to both the quality and length of life. If one considers the most popular dog breeds in the United States, the top 10 include the Labrador Retriever, German Shepherd, Golden Retriever, French Bulldog, Beagle, Poodle, Rottweiler, Yorkshire Terrier, and German Shorthaired Pointer. Some idea of the pervasiveness of genetic defects across breeds can be gleaned from a recent book detailing genetic predisposition to disease. The book contains 93 pages of references. The list of diseases for the most popular dog, the Labrador Retriever, is 6.25 pages long. Yet, despite the tragic consequences of such diseases in animals regarded as beloved family members, breed standards associated with these diseases remain unchanged. This represents a major tragedy to which insufficient attention is paid. The point of this paper is to show that even as dogs have increasingly become viewed as "members of the family", this status is belied by the proliferation of genetic diseases perpetuated by breed standards.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".