93 Feeding behavior as predictive traits of fertility and lifetime productivity in replacement beef heifers
Bibliographic record
Abstract
Abstract Fertility and longevity traits in beef cows are difficult and expensive to measure. The use of other traits to predict fertility and longevity traits in beef cows will enable selection for efficient reproduction earlier in an animal’s life. The objective of this study is to identify feeding behaviours with predictive relationships to fertility, longevity, and lifetime productivity in beef cows. This study used 421 Bos taurus commercial replacement heifers born from 2004 to 2014 and followed over 1050 mating opportunities, 11 production cycles and 5 parities. Heifers were weaned at 6–7 mo of age and developed on a 90% barley silage, 10% rolled barley diet in pens equipped with 16 electronic feed bunks to monitor feed intake and feeding behaviors. Feed intake (FI), feeding frequency (FREQ), duration (DUR), head-down time (HD) and time-to-bunk (TTB) were collected over the course of the feeding trial. Phenotypic correlations indicate that as FI increased during the heifer development stage, so did subsequent cow pre-breeding and pre-calving body condition score (BCS; r = 0.43, 0.36 respectively) and total lifetime productivity (r = 0.40). As FREQ increased, pre-breeding and pre-calving BCS decreased (r = -0.16 and -0.20, respectively), as did calf birth weight (r = -0.15). As DUR increased, weak positive correlations were observed with days in herd (r = 0.10), age at first calving (r = 0.17) and calf birth weight (0.13). Increased HD would lead to increased pre-breeding and pre-calving BCS (r = 0.16, 0.23 respectively), and greater lifetime productivity (r = 0.16). Time-to-bunk was positively correlated with days in the herd (r = 0.15), and with decreased calf birth weight (r = -0.23) and pre-calving cow weight (r = -0.33). These feeding behaviour traits, collected early in life, are worthy of further investigation into multi trait regression, genetic correlations and genomic selection.
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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 | 0.000 | 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) | 0.001 | 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".