Effectiveness of the use of the Vytelle system in evaluating bulls in Kazakhstan
Bibliographic record
Abstract
This article presents the results of the introduction of Canadian digital technologies Vytelle GrowSafe in the beef cattle breeding of Kazakhstan to assess Residual Feed Intake (RFI). The experiments were carried out with Qazaq Aqbas Breed in the Privet Farms of North Kazakhstan (Akmola and Pavlodar farms). As part of the ration in Akmola farm for a bull of 350 kg of weight, are contained 77 MJ or 7.6 kg of DM, in Pavlodar farm for a bull weighing 290 kg contains 65 MJ or 6.5 kg of DM. Studies have shown that in the Akmola farm the RFI index ranged from -2.08 to +2.88, in the Pavlodar farm from -1.89 to +2.06. According to the results of research in the Akmola farm, the RFI assessment show, 7 out of 26 bulls are suitable for breeding sale, in the Pavlodar farm out of 55 bulls 18. The assessment of the productive effect of feed on RFI, mainly (by 80%), coincided with the assessment of the ratio of gain to consumed dry matter, and some bulls showed deviations in terms of residual average daily live weight gain and the corrected value of the ratio of feed costs to live weight gain, which led to incomplete manifestation of the productive potential of the consumed feed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".