Half‐sib family selection for forage yield in orchardgrass
Bibliographic record
Abstract
Abstract Genetic variation for forage yield of orchardgrass is abundant, but there are few reports of progress from selection for increased forage yield. The objective of this study was to estimate direct effects of selection from one cycle of half‐sib family selection for forage yield in orchardgrass. Eleven selected populations were compared with their parent populations within three maturity groups. Populations were evaluated under hay management at three locations and management‐intensive rotational grazing at two locations. Nine of the 11 selected populations differed, by an average of 7.4%, from their parent population in forage yield. Nine of the selected populations also showed changes in Drechslera leafspot reaction, all indicating a negative genetic correlation with forage yield. Selection for high forage yield tended to result in greater ground cover and later relative maturity. However, changes in net herbage accumulation (NHA) under rotational grazing were generally not significant and were uncorrelated with changes in forage yield, indicating that forage yield of hay plots is not correlated with the NHA of grazed plots. Although genetic gains in forage yield measured under hay management were very favourable relative to other reports from the literature, the lack of correlated progress under grazing management indicates that directed selection for NHA of orchardgrass should be conducted under grazing management.
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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".