Bibliographic record
Abstract
Dear Sir, I wish to comment on Dr. Rollin's response to the May 2003 ethical question of the month on the elastrator for older bulls (Can Vet J 2003;8:624). First, on what scientific facts does Dr. Rollin base his statement “... of all the methods of castration ... banding is probably the most painful method”? Has Dr. Rollin or anyone he knows placed an elastrator band on his or her finger for hours to eloquently demonstrate the nature of the pain, as he suggests? Second, I question the statement “... there is no reason to believe that castration is any less painful in a newly born calf than in an older animal, ...” Is this statement based on fact or conjecture? After 20 years in large animal practice, my clinical impression is that the younger the animal the better tolerated invasive procedures are, and the faster the healing time. I am not endorsing banding; however, I am endorsing science and scientific fact rather than personal opinion based on emotion and perception. The Canadian Veterinary Journal should strive to reflect the science of the veterinary profession and not emotions — which is the edict of People for the Ethical Treatment of Animals (PETA). H.J. Rumney, BSc (Bio) BSc (Agr), DVM 1831 Rumney Road Tay Township, RR #1 Midland, Ontario L4R 4K3
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 | 0.006 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.023 | 0.017 |
| Insufficient payload (model declined to judge) | 0.158 | 0.148 |
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".