Hot Iron Branding of Beef Cattle: Process Characterization, Implications for Animal Welfare, and Its Efficiency for Cattle Individual Identification
Bibliographic record
Abstract
This study aimed to characterize the hot iron branding (HIB) procedure by assessing its implications for animal welfare and its efficiency for cattle identification. The study was carried out in two stages: First, with 37 Nellore calves, by measuring the skin temperatures in the place of HIB application (ONB) and 10 cm above it (OFFB) immediately after its application and during four consecutive days, the time required for application of each HIB digit and the occurrences of rebranding; second, with two batches of cows (N = 97 and N = 94, respectively, by measuring the time spent to read cattle ID and comparing the efficiency of HIB vs. EET (electronic ear tag) and visual ear tags (VET) vs. EET. Skin temperature was significantly affected by the interaction between the place where the skin temperatures were taken (on and 10 cm above the HIB) and assessment day, with temperatures in ONB on days d0 and d2 being higher than in OFFB (p < 0.05), and 86% of the calves required at least one rebranding. EET reading was faster than HIB and VET (p < 0.001), and fewer errors were made when reading EET than HIB (1/97 vs. 17/97) and VET (2/94 vs. 12/94). We concluded that HIB potentially compromises cattle welfare and has a lower efficiency for cattle identification than EET and VET.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".