PSVII-21 Canadian Vytelle technology for determining residual feed intake epdsin raising Qazaq Aqbas bull calves
Bibliographic record
Abstract
Abstract The Abstract reviews the experience of implementing Vytelle technology in ZhanaBereke LLP, Akmola region in Kazakhstan. The trial objects are Qazaq Aqbas bull calves: group 1 aged 10–11 months (n = 22), group 2 aged 11–12 months (n = 24). Data analysis showed that more reliable Residual Feed Intake calculated for each individual animal on the basis of their phenotypes and information on pedigree, were found in group 1. The average RFI EPD for group 1 is -0.0607, for group 2 is -0.0297. The RFI% Rank in terms of RFI EPD for both trial groups varied within 4 ... 96%. However, the average RFI% Rank was lower in the first group (45%) compared to the second group (56%). From which it follows that the RFI EPD is higher in group 1. Average Daily Gain EPDs (ADG EPD) higher in group 1 -0.0018. A higher ADG EPDs indicate a more cost-effective metric. The ADG% Rank in terms of ADG EPD for group 1 varied within 1 ... 62%, for group 2: 1 ... 63%. The average ADG% Rank was slightly lower in the first group (30%) compared to the second group (33%). From the presented data, it follows that the breeding valueaccording to the ADG EPD is higher in group 1. The average Dry Matter Intake by animals per day during the trial (DMI EPD) is equal to -0,0600 in the first group, and -0,0292 in the second group. The DMI% RANK in terms of DMI EPD for group 1 varied within 31 ... 93%, for group 2: 38 ... 96%. The DMI% RANK was lower in the first group (60%) compared to the second group (67%). Summarizing the above, we can conclude that selection for this characteristic is less effective, work will continue to increase this indicator.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".