Replication Data for: The effects of hot-iron application method on wound characteristics and healing in dairy calves
Bibliographic record
Abstract
Hot-iron disbudding is the most commonly used method for horn bud removal in dairy calves. While factors such as post-treatment care, calf age, and hot-iron brand have been studied in relation to wound healing, procedural factors—such as application time and tip size—have received little attention. The objective of this study was to evaluate the effects of hot-iron application time and tip size on wound characteristics and healing progression. Female Holstein calves (n = 24) were randomly assigned to one of two application times: 10 s vs. 20 s. Within each calf, one horn bud was disbudded using a small tip and the other with a large tip. Disbudding was performed at 28 d of age using multimodal pain management (sedation, local nerve block, and analgesics). Wound size and depth were measured on the day of disbudding and every three days thereafter until the wounds had fully re-epithelialized. Wounds were also photographed for classification of tissue type. Wounds resulting from the large tip were larger and shallower than those created with the smaller tip, requiring more time to re-epithelialize (51.9 ± 2.04 d vs. 45.3 ± 2.04 d). Wounds from the 20-s application were larger and deeper than those from the 10-s application, but this did not result in increased time to re-epithelialization. Evidence of horn development after disbudding was observed in 15 of the 24 buds treated with the small tip versus 3 of the 24 treated with the larger tip. These findings suggest that larger tips increase wound size and healing times, and that a 10-s application time is sufficient to prevent horn growth.
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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 | 0.023 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.111 | 0.020 |
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".