Breeding meadow bromegrass for forage characteristics under a line-source irrigation design
Bibliographic record
Abstract
Production from less productive lands limited by irrigation can be increased if genetically improved pasture grasses are developed with increased dry matter production (DMY) and nutritional quality. In 2000, 18 half-sib families of meadow bromegrass were seeded in a modified strip-plot design with four replications and water levels (WL) applied as nonrandom strips ranging from 10.1 mm wk-1 at WL-5 to 36.8 mm wk-1 at WL-1. The objective was to estimate genetic variability and parameters as affected by irrigation level and harvest date for DMY, crude protein (CP), in vitro true digestibility (IVTD), neutral detergent fiber (NDF), and digestible neutral detergent fiber (dNDF). Low h2 estimates for DMY suggest that gains in total DMY from selection within these half-sib families (HSF) are not likely. Crude protein concentrations were more influenced by harvest date than WL. Heritability estimates were relatively high regardless of WL or harvest date for IVTD. The effect of WL on h2 estimates for NDF were less defined, suggesting that gains might be achieved faster if selection was done on forage harvested later in the growing season at less than optimum irrigation. Heritability estimates for dNDF were either small or associated with large standard errors. Key words: Heritability, irrigation rates, forage yield and quality, crude protein, neutral detergent fiber, in vitro true digestibility
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".