Detection of yield-independent forage quality differences among timothy genotypes
Bibliographic record
Abstract
The trade-off between forage yield and quality would be minimized by selecting a genotype that produces high-quality forage regardless of yield. This paper attempts to detect forage quality differences among timothy (Phleum pratense L.) genotypes that are independent of yield. Two separate field experiments were conducted from 1993 to 1995 on an Ando loamy sand in Hokkaido, Japan. For exp. 1, timothy plants (cv. Nosap) were harvested at various maturity stages for the first, second, and third cuts in 1994 and 1995 to evaluate the relationships of crude protein (CP) and neutral detergent fibre (NDF) yields with dry matter (DM) yield. The relationship between NDF and DM yields was positive and linear across different cuts (r2 = 0.98–0.99), and was slightly affected by year. In contrast, the relationship between CP and DM yields depended on the cuts (r2 = 0.01–0.98). In exp. 2, four genotypes were evaluated in 1994 to compare the coefficients of the NDF vs. DM yield regressions using ANCOVA. The comparison showed that the NDF yields of Kitami 20, Kitami 21, and Hokuo were less than that of Nosap (P < 0.01) for a wide range of DM yields (150–700 g m-2). Thus, NDF concentrations were lower in the former genotypes across varying forage yields. Using the regression lines as indices could facilitate selection of timothy genotypes that reduce the trade-off between yield and quality, although further experiments are needed to confirm the usefulness of this method. Key words: Acid detergent fibre, crude protein, forage yield, neutral detergent fibre, timothy
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".