Ensiling barley cultivars selected for varied levels of in vitro neutral detergent fiber digestibility in mini- and bunker-silos to evaluate effects on fermentation.
Bibliographic record
Abstract
Thirty hour in vitro neutral detergent fiber digestibility (NDFD) of three barley cultivars such as ‘CDC Cowboy’ [high-neutral detergent fiber digestibility (H-NDFD)], ‘CDC Copeland’ [intermediate-neutral detergent fiber digestibility (I-NDFD)], and ‘Xena’ [low-neutral detergent fiber digestibility (L-NDFD)] was ranked from 80 commercial silage samples. Cultivars were seeded on the same day, harvested at mid-dough, and ensiled in mini or bunker silos. Mini silos were sequentially opened over 60 d, and day 60 samples were exposed to air for 21 d. Bunker silos were sampled after 60 d. Cultivars did not differ in NDFD, while terminal pH was lower (P < 0.01) for H-NDFD than other silages. The pH of H-NDFD was greater (P < 0.01) and pH of I- and L-NDFD lower (P < 0.01) in bunker than mini silos. Lactate and acetate were greater (P < 0.05) for H-NDFD in mini silos, with lower (P < 0.01) acetate in mini than bunker silos. Intermediate NDFD mini silos were greater (P < 0.01) in acid detergent fiber (ADF) and NDF compared with other silages, traits which were also greater (P < 0.01) for H-NDFD and L-NDFD in bunker vs. mini silos. High-NDFD silage was less aerobically stable than other silages. Using NDFD of field silage samples to select barley silage cultivars for improved NDFD is not a viable strategy based on the results of this study.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".