Collation of Data and Genetic Parameter Estimation in Different Experimental Canadian Beef Cattle Populations Measured for Feed Efficiency
Bibliographic record
Abstract
Improvement in feed efficiency (FE) can contribute to a large increase in profitability of a beef production system but its measurement requires considerable expense and time. Hence, there is significant merit in combining existing FE databases for further genetic analyses. Four experimental datasets were collated from the University of Alberta, University of Guelph, Alberta Agriculture and Rural Development, and Agriculture and Agri-Food Canada which summed to 7317 FE records after edits. Residual feed intake (RFI) and residual intake and gain (RIG) were calculated across the entire dataset as measures of FE. (Co)variance components were estimated between datasets. Heritability of RFI across all datasets was 0.41 and varied from 0.29 to 0.48 within dataset. Genetic correlations between datasets for RFI ranged between 0.77 and 0.86 indicating that it is appropriate to pool data from the aforementioned datasets. Similarly, genetic correlations for RIG ranged from 0.75 to 0.85.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".