Development of the laser remote caliper as a method to estimate surface area and body weight in beef cattle
Bibliographic record
Abstract
Linear measurements combined with surface area and volume calculations were used to develop formulas to estimate body weight (BW) in beef cattle. These measurements were evaluated directly or estimated using a laser remote caliper (LRC) and digital imaging software. Seventy-two dry, late gestation beef cows aged 3-13 years were measured and weighed Six measurements for each cow were taken; the cattle were weighed, a body condition score (BCS) was assigned, heart girth (HG), hip width (HW), and hip height (HH) were measured directly and 3 digital pictures were taken. The digital pictures portrayed three different views; side view (restrained), rear view (restrained), and side view (free-stall). Body length, HW, HH, surface area and volume were indirectly calculated from the digital images. For each view a complete (C-) formula (direct and indirect measures) and remote (R-) formula (only indirect measures) to estimate BW was developed. The R-squared values 0.7459, 0.7937, 0.8078, 0.5016, 0.611, 0.5553 were attained for C-side view free-stall, C-side view (restrained), C-rear view (restrained), R-side view free-stall, R-side view (restrained), and R-rear view (restrained). The accuracy of these formulas was 81% on average. BCS, HG and HW were the most significant factors when developing a formula for BW (p-value < 0.001). Side view (restrained) image measurements were most accurate in estimating BW. These measurements were highly correlated with the direct measurements and digital linear body measurements were not distorted (due to poor posture/positioning) as seen with the other views. The results or this study show that linear measurements collected by digital imaging methods can be a useful tool for estimating BW.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".