In vitro ruminal undegradable proteins of alfalfa cultivars
Bibliographic record
Abstract
The quality of alfalfa would be greatly improved by an increase in its ruminal undegradable protein (RUP) concentration. Protein degradation rate (PDR), in vitro dry matter digestibility (IVDMD), leaf weight ratio (LWR), dry matter yield (DMY), total nitrogen (TN), in vitro RUP (expressed on both TN, RUP-TN, and dry matter basis, RUP-DM), acid detergent fiber (ADF), and neutral detergent fiber (NDF) concentrations were determined in 27 alfalfa cultivars. Cultivars were seeded in triplicate on 2 consecutive years and evaluated during the 2 subsequent production years with two harvests per year. Protein degradation rate and RUP-TN were determined using a ruminal inhibitor in vitro system. Data were averaged for spring growth, summer regrowth, and both harvests across 2 production years. Each of the three data sets was analyzed by ANOVA followed by a principal component analysis (PCA) on the ANOVA means. For the four-harvest data, cultivar differences were highly significant (P < 0.001) for all variates except for PDR (P = 0.07) and RUP-TN concentration (P = 0.10). The first PCA axis was largely defined positively by RUP-DM, IVDMD, TN, LWR, and RUP-TN, but negatively with ADF, NDF, PDR, and DMY. The second PCA axis defined a contrast between PDR versus RUP-TN, DMY, ADF, and NDF. Five cultivars were distinctive with high or low PCA scores in all three PCA. Rangelander and Heinrichs, along with Ultra, had low PDR; the first two cultivars had low DMY whereas Ultra was a medium-yielding cultivar. In contrast, Algonquin and Oneida VR had high PDR and medium DMY. While the first principal component (PC) indicated a general trend that low PDR and high RUP were associated with low-yielding cultivars, the second PC identified specific cultivars with both low PDR and high DMY. Therefore, selection for low PDR and high DMY is feasible. Key words: ruminal protein escape, dry matter digestibility, alfalfa
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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) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".