Fermentation, degradation and microbial nitrogen partitioning for three forage colour phenotypes within anthocyanidin‐accumulating <i>Lc</i>‐alfalfa progeny
Bibliographic record
Abstract
BACKGROUND: Alfalfa has the disadvantage of having a rapid initial rate of protein degradation, which results in pasture bloat, low efficiency of protein utilisation and excessive nitrogen (N) pollution into the environment for cattle. Introducing a gene that stimulates the accumulation of monomeric/polymeric anthocyanidins might reduce the ruminal protein degradation rate (by fixing protein and/or direct interaction with microbes) and additionally reduce methane emission. The objectives of this study were to evaluate in vitro fermentation, degradation and microbial N partitioning of three forage colour phenotypes (green, light purple-green (LPG) and purple-green (PG)) within newly developed Lc-progeny and to compare them with those of parental green non-transgenic (NT) alfalfa. RESULTS: PG-Lc accumulated more anthocyanidin compared with Green-Lc (P < 0.05), with LPG-Lc intermediate. Volatile fatty acids and potentially degradable dry matter (DM) and N were similar among the four phenotypes. Gas, methane and ammonia accumulation rates were slower for the two purple-Lc phenotypes compared with NT-alfalfa (P < 0.05), while Green-Lc was intermediate. Effective degradable DM and N were lower in the three Lc-phenotypes (P < 0.05) compared with NT-alfalfa. Anthocyanidin concentration was negatively correlated (P < 0.05) with gas and methane production rates and effective degradability of DM and N. CONCLUSION: The Lc-alfalfa phenotypes accumulated anthocyanidin. Fermentation and degradation parameters indicated a reduced rate of fermentation and effective degradability for both purple anthocyanidin-accumulating Lc-alfalfa phenotypes compared with NT-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) | 0.000 | 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) | 0.000 | 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".