292 Genetic trend for feed efficiency, growth and carcass traits in three Canadian beef cattle populations involved in the Kinsella breeding project.
Bibliographic record
Abstract
Genomic selection for feed efficiency in beef cattle offers an opportunity to reduce feed costs and greenhouse gas (GHG) emissions while increasing profitability and environmental sustainability in beef production. Data from a selection experiment using three beef cattle populations maintained at the University of Alberta Roy Berg Kinsella Research Ranch namely, the Kinsella beef composite (KC - split into efficient and control lines), purebred Angus (AN), and purebred Charolais (CH) were analyzed following three years of selection (2013–2016) to obtain an evaluation of selection responses. Selection was carried out using multiple trait selection indexes based on molecular breeding values (MBVs). For the KC and AN population, a maternal profitability index (MPI) for improved feed efficiency, i.e. low residual feed intake (RFI), higher direct (DWWT) and maternal weaning weights (MWWT) was applied, while for the CH population a feedlot profitability index (FPI) for low RFI, low dry matter intake (DMI), higher average daily gain (ADG), greater carcass marbling (CMAR), greater hot carcass weight (HCW), increased lean meat yield (LMY), and increased average back fat thickness (AFAT) was applied. Across the three populations, average estimated breeding values (EBVs) obtained from a multivariate animal model showed significant differences at P<0.05 between the 2013 and 2016 born calves for some traits in the selection indexes. In the KC population, the 2016 KC-Efficient herd had a significantly lower average EBV for RFI of -0.004 ± 0.015 Kg DMI/d and a higher average MWWT of 0.018 ± 0.003 kg in comparison to 0.121 ± 0.014 kg DMI/d for RFI and 0.011 ± 0.003 kg for MWWT in the 2016 KC-Efficient calves. Average EBVs for DWWT and MWWT increased in the AN population from -2.071 ± 0.987 to 1.949 ± 0.948 kg and -0.76 ± 0.38 to 1.722 ± 0.317 kg respectively across years. In the CH population, LMY increased from -0.448 ± 0.156 to 0.004 ± 0.117% while CMAR decreased from 16.61 ± 3.247 to 1.377 ± 2.153.
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 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".