Body condition score and its relationship to ultrasound backfat measurements in beef cows
Bibliographic record
Abstract
Spring calving beef cows from two genotypes were used in two different trials to examine the effectiveness of visual scoring systems to predict body condition. In trial 1, data on body condition score and ultrasound backfat measurements were collected at three different stages of the production cycle: dry, nursing and weaning. Scoring was by three different assessors and ultrasonic measurements by one experienced technician. Visual scores were positively but inconsistently related to ultrasound measurements (R2 = 0.14, 0.27 and 0.41 for dry, nursing and weaning times, respectively). In the second trial, two different subjective scoring methods were studied, general condition score based on an overall visual assessment of condition with scores ranging from 1 to 5, increasing in half units, and a specific site score based on visual assessment of condition identified at six specific sites on the animal’s body, with scores ranging from 6 to 30. Differences in scores between levels of experience which were observed with the general method were removed with the specific site method. Precision of estimating ultrasound measurements (R2) was improved from 54% for the general to 64% for the specific site assessment when scoring was by experienced assessors. Visual assessment could be improved by more specific scoring, although for research purposes visual assessment would still be inadequate in measuring condition relative to ultrasonic measurements. Key words: Beef cows, ultrasound measurements, condition scoring
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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 | 0.003 |
| 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 | 0.000 | 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".