Divergent phenotypic selection for alfalfa cell wall fractions and indirect response in digestibility
Bibliographic record
Abstract
An alfalfa (Medicago sativa L.) breeding strategy to decrease slowly digestible or indigestible fiber and simultaneously increase digestible fiber could improve forage quality without reducing total fiber. The objectives were: (1) to estimate selection responses from divergent and opposite direction selections of (i) hemicellulose (HEM) and acid detergent fiber (ADF), (ii) acid detergent lignin (LIG) and HEM + cellulose (CEL) and (iii) CEL and HEM + LIG, and (2) to determine correlated responses in in vitro true digestibility (IVTD). Selection progress was evaluated in replicated plot trials at two locations, sampled for 2 or 3 yr. Selection for divergent HEM and ADF resulted in change only for ADF [10.9 g kg-1 dry matter (DM)]. Selection for divergent LIG and HEM + CEL, resulted in same direction change in LIG (3.3 g kg-1 DM). Selection for divergent CEL and HEM + LIG resulted in change only in CEL (5.1 g kg-1 DM). Low LIG and high HEM + CEL, and low ADF and high HEM populations had 9.7 and 8.3 g kg-1 DM higher IVTD than their counterparts, respectively. The first cycle of selection for the fiber components simultaneously in the opposite directions was not successful. However, reduced LIG or ADF concentration appears to increase alfalfa forage digestibility and decrease total fiber concentration. Key words: Alfalfa, cell wall, hemicellulose, cellulose, lignin, 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 | 0.000 | 0.000 |
| 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".