Using body measurements to estimate body weight in gilts
Bibliographic record
Abstract
The absence of a scale on pig farms has led to indirect body weight (BW) estimation using regression models based on body measurements. The objectives of the present study were to (1) develop prediction equations for weight estimation in gilts using body measurements (FF: flank-to-flank distance; L: length; HG: heart girth; BF2: ultrasound backfat measurement; LD: loin depth; and BCS: body condition score) and (2) validate the use of an existing prediction equation for BW in gilts (HG 2 × L × 69.3 = HGLW), only used for finishing pigs. Data set A (derivation, Large White × Landrace) included 42 gilts at first insemination, 45 gilts at the end of first gestation, and 58 gilts at weaning. Data set B (validation, Large White × Landrace) comprised of 14 gilts at first insemination, 15 gilts at the end of first gestation, and 19 gilts at weaning. Models were developed for each physiological state but a better BW prediction was obtained from an overall model, including an adjustment for physiological state (S1 and S2): −168.89 +1.06L +1.28HG +58.02S1 +33.03S2 +10.92BCS −1.10BF2 (adjusted R 2 = 0.90). This model was validated under conditions found in the present study. Estimations using HGLW showed greater residual means than regression models.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".