Calf Response to Caustic Paste and Hot-Iron Dehorning Using Sedation With and Without Local Anesthetic
Bibliographic record
Abstract
Dairy producers require effective methods of reducing the pain associated with dehorning. Previous work has shown that analgesics can reduce pain associated with hot-iron dehorning, but these interventions may not be practical for some producers. Some producers favor the use of caustic paste (to cause a chemical burn as opposed to thermal burn), but little is known about how to treat pain caused by caustic burns. In 2 experiments, head shaking and head rubbing behaviors were used to evaluate pain following dehorning using caustic paste. In experiment 1, sedated calves were dehorned using a caustic paste, with or without a lidocaine local block. Calves treated with lidocaine showed no evidence of reduced pain response in the hours after the caustic paste was applied. In experiment 2, response to caustic paste dehorning with a sedative only was compared with hot-iron dehorning using a sedative and local anesthetic. In the first 4 h after dehorning, calves dehorned with a hot iron showed more head shaking than calves dehorned with caustic paste. These results indicate that dehorning with caustic paste combined with a sedative results in less pain to calves than dehorning with a hot iron combined with a sedative and a local anesthetic.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".