Where is the potential with hoggets?
Bibliographic record
Abstract
The FarmIQ programme provided a great opportunity to explore new technologies and opportunities. At the Landcorp Stuart Farm in the Te Anau basin, Luke wright and his team chose to explore the opportunity to improve the performance of their lambing hoggets. Paul McGill, as the Landcorp FarmIQ director, as able to provide some analysis of the figures, and track the on-going performance of the hoggets as they went on to enter the ewe flock. We used the opportunity to see if we could use lucerne during lactation to make sure we had better lamb weaning weights, and that the hogget went on to be a good 2TH.\n\nThe first thing that we saw over the 2 year project was that hoggets on lucerne were able to gain weight, even while rearing a lamb, and a lot of this extra weight gain was during early lactation. Another property that emerged from this work was that when we could take the pressure off the lambing hogget on standard pasture (by putting some of the hoggets on lucerne) then we could get good live weights from them as well. Our hoggets were at 2TH mating weights by the time we weaned their lamb.\n\nInterestingly, the lambs on lucerne grew about the same rate as lambs on pasture. There were a couple of important points to note here. In both years, the lambs on lucerne were heavier than their pasture counterparts at docking, probably due to the intake of the hogget, producing more milk. The management of the lucerne when the lambs started eating was really important. In the first year when the growth was rapid and quite ‘soft’ (with a short rotation) then the lambs did not do quite so well and developed photosensitivity symptoms. In the second year, when we made the rotation length longer and ‘hardened the feed off’, we saw much better lamb weaning weights. So we need to make sure that the rotation lengths are kept reasonably long (30-35 days) rather than short (20-24 days).\n\nThe stocking rate on the lucerne was 2.3 hoggets higher than it was on pasture (at about 9.4), and was more consistent between years. However, the lamb losses were higher on the lucerne, for reasons we don’t fully understand. However, the result was consistent in both years. Overall, the productivity from the lucerne was much greater than from the pasture.\n\nFinally, when we followed the hoggets on into their 2TH year, we found that, although there wasn’t a great difference in tupping live weight (66 vs 64kg), the hoggets that were on lucerne (204%) did scan better than those that had been on pasture (187%) during lactation.
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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) | 0.105 | 0.002 |
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; both teacher heads agree on what is shown here.
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".