Why Veterinary Medicine and Veterinary Medical Education Needs an Accurate Census of Companion Animals by Household for the United States
Bibliographic record
Abstract
Just how many veterinarians need to be trained for companion-animal care in the United States? One might answer, ‘‘Enough but not too many’’; but how many are enough? The answer to that question clearly depends on how many companion animals there are in the United States, and that is the problem. No one knows just howmany companion animals there are in the United States, and until we do know, it is nearly impossible to predict just how many companionanimal veterinarians we should be educating. Knowing the total number of companion animals in the United States would be a very helpful start. Taking the data further and matching the pet population to the demographics of the pet-owning human population would provide even more constructive information upon which to build a plan of care and, from there, a plan for educating the number of companion-animal veterinarians that is truly needed and for determining the varying nature of their expertise.
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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 | 0.004 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".