Prediction of within-herd differences in total feed intake between growing pigs
Bibliographic record
Abstract
Electronic feeders for measurement of individual feed intake in group-penned pigs have been available for many years, and have been used to measure and select breeding pigs for feed efficiency. The cost per feeder is usually too high to measure intake of all selection candidates in a nucleus herd over the whole grower-finisher period. If the feed intake of each candidate is measured over only part of the period, this would allow more pigs to be measured per feeder in a given time. This paper documents an analysis of test station data on Yorkshire, Landrace, Duroc and crossbred pigs, estimating the accuracy of prediction of total feed intake based on intake measured over different parts of the grower-finisher period. Feed intake measured over only about 2 wk from 80 to 90 kg liveweight explained 50% of the variance in total feed intake from 30 to 100 kg liveweight, in a dataset independent from the one used to derive the parameters of the prediction. Of all possible periods spanning 10 kg of liveweight gain, this measurement period was the most accurate, and coincided with the period of maximum growth rate. It is concluded that total feed intake over the grower finisher period can be predicted with useful accuracy, using feed intake measured over a period of about 2 wk or 10 kg of liveweight gain from around 80 to 90 kg. The gain in accuracy achieved by measuring pigs over multiple periods (e.g., from 50 to 60 kg and from 80 to 90 kg) is much smaller than the initial benefit of recording over just 10 kg of gain. Key words: Swine, feed efficiency, feed conversion
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 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".