Season of testing and its effect on feed intake and efficiency in growing beef cattle1
Bibliographic record
Abstract
This study sought to assess whether residual feed intake (RFI) calculated by regressing feed intake (DMI) on growth rate (ADG) and metabolic mid-BW in 3 different ways led to similar estimates of genetic parameters and variance components for young growing cattle tested for feed intake in fall and winter seasons. A total of 378 beef steers in 5 cohorts were fed a typical high energy feedlot diet and had free-choice access to feed and water. Feed intake data were collected in fall or winter seasons. Climate data were obtained from the University of Alberta Kinsella meteorological station and Vikings AGCM station. Individual animal RFI was obtained by either fitting a regression model to each test group separately (RFI(C)), fitting a regression model to pooled data consisting of all cohorts but including test group as a fixed effect (RFI(O)), or fitting a regression to pooled data with test group as a fixed effect but within seasonal (fall-winter or winter-spring) groups (RFI(S)). Two animal models (M1 and M2) that differed by the inclusion of fixed effects of test group or season, respectively, were used to evaluate RFI measurements. Feed intake was correlated with air temperature, relative humidity, solar radiation, and wind speed (-0.26, 0.23, 0.30, -0.14 for fall-winter and 0.31, -0.04, 0.14, 0.16 for winter-spring, respectively), but the nature and magnitude of the correlations were different for the 2 seasons. Single trait direct heritability, model likelihood, direct genetic variance, and EBV accuracy estimates were greatest for RFI(C) and least for RFI(O) for both M1 and M2 models. A significant genetic correlation was also observed between RFI(O) and ADG, but not for RFI(C) and RFI(S). Including a season effect (M2) in the genetic evaluation of RFI(O) resulted in the smallest heritability, model LogL, EBV accuracy, and largest residual variance estimates. These results, though not conclusive, suggest a possible effect of seasonality on feed intake and thus feed efficiency.
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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 | 0.001 | 0.001 |
| 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".