Estimates of genetic parameters for rumination time, feed efficiency, and methane production traits in first-lactation Holstein cows
Bibliographic record
Abstract
The large-scale recording of traits such as feed efficiency (FE) and methane emissions (ME) for use in genetic improvement programs is complex, costly, and time-consuming. Therefore, heritable traits that can be continuously recorded in dairy herds and are correlated with FE and ME traits could provide useful information for genetic evaluation. Rumination time has been suggested to be associated with FE, methane production (MeP; ME in g/d), and production traits at the phenotypic level. Therefore, the objective of this study was to investigate the genetic relationships among rumination time (RT), FE, methane and production traits using 7,358 records from 656 first-lactation Holstein cows. The estimated heritabilities were moderate for RT (0.45 ± 0.14), MeP (0.36 ± 0.12), milk yield (0.40 ± 0.08), fat yield (0.29 ± 0.06), protein yield (0.32 ± 0.07), and energy-corrected milk (0.28 ± 0.07), but were low and nonsignificant for FE (0.15 ± 0.07), which was defined as the residual of the multiple linear regression of DMI on energy-corrected milk and metabolic body weight. A favorable negative genetic correlation was estimated between RT and MeP (-0.53 ± 0.24), whereas a positive favorable correlation was estimated between RT and energy-corrected milk (0.49 ± 0.11). The estimated genetic correlation of RT with FE (-0.01 ± 0.17) was not significantly different from zero but showed a trend of a low correlation with dry matter intake (0.21 ± 0.13). These results indicate that RT is genetically associated with MeP and milk production traits, but high standard errors indicate that further analyses should be conducted to verify these findings when more data for RT, MeP, and FE become available.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".