Optimal use of on-farm fibre diameter measurement and its impact on reproduction in commercial Merino flocks
Bibliographic record
Abstract
The use of on-farm measurement of fibre diameter in clip preparation and selection was examined using a stochastic simulation. Commercial self-replacing Merino flocks were simulated, with each flock equivalent in stocking rate to a 2500-ewe flock with an average fibre diameter of 19 µm (with the same energy requirements as 4250 50-kg dry sheep). Returns from each selection and clip preparation strategy were based on a 5-year period from 1998 to 2003. The percentage of sales where it would have been profitable to utilise fibre diameter measurement in wool classing declined from 84% to less than 1% in 19- and 25-µm flocks, respectively. Clip preparation benefits reduced from AU$5.52 to $1.46 per dry sheep equivalent as the mean fibre diameter of the flock increased from 19 to 25 µm (under random selection). Selection on either fibre diameter or selection index (12% micron premium based on fibre diameter and fleece weight) was of benefit for 19- and 21-µm flocks, but not broader fibre diameter flocks. Flock reproductive performance was simulated at the individual animal level, based on the traits of fertility (ewes lambing per ewe joined), litter size (lambs born per ewe lambing) and lamb survival (lambs weaned per lamb born). These traits were simulated with genetic components, allowing the investigation of correlated responses in reproduction traits. Single-trait selection on fibre diameter did not significantly impact on reproduction traits but did reduce fibre diameter, fleece weight and bodyweight. There was a large variation in the returns from clip preparation, as the mean fibre diameter of the flock increased, and over selling periods, highlighting the importance of understanding the current market conditions and their relationship with the flock mean fibre diameter when considering the use of fibre diameter in clip preparation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".