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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 teacher head, 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".