PSIX-1 Canadian Vytelle technology for determining residual feed intake in raising Qazaq Aqbas bull calves
Bibliographic record
Abstract
Abstract Selection for residual feed intake (RFI) allows you to reduce feed costs and increase productivity of beef cattle. An increase in feed productivity by 10% can lead to an increase in profits by 43%, raising livestock with a low RFI can reduce feed intake by 12%, reduce methane emissions by 30%, and manure reduction by 17%. To obtain reliable trial results when determining the RFI, it is necessary to ensure the following conditions: 1) the same age of animals 2) the exchange of pedigree data between users of the system, which makes it possible to compare the EPDs within Vytelle Systems. Objects of research:QazaqAqbas bull calves (n = 46) at the age of 10–12 months in ZhanaBerekeLLP in Akmola region of Kazakhstan. Trial results confirm that residual feed intake in group 1 varied from -0.81 to 1.11, in group 2 - from -0.80 to 1.09. The RFI Rank was higher in group 1 (12.5). RADG in group 1 was at the level of -0.57 ... 0.58, in group 2 - -0.58 ... 1.13. According to the numerical rating of the animal (RADG Rank), the average value in group 1 was 12.5, in group 2 - 11.5. The average live weight at the beginning (START WT.) and end (END WT.) in the first group was 254.16 and 287.62 kg, in the second group 239.99 and 273.09 kg. The ADG in two groups was at the same level - 0.70 and 0.69 kg. The average Dry Matter Intake per day by animals during the trial was higher in the first group - 4.15, in the second group it was 3.65. For the first time in Kazakhstan national QazaqAqbas breed is tested for RFI, RADG, ADG, DMI, Raw F:G, Adj F:G.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".