The Use of Gene Modification and Advanced Molecular Structure Analyses towards Improving Alfalfa Forage
Bibliographic record
Abstract
Alfalfa is one of the most important legume forage crops in the world. In spite of its agronomic and nutritive advantages, alfalfa has some limitations in the usage of pasture forage and hay supplement. High rapid degradation of protein in alfalfa poses a risk of rumen bloat to ruminants which could cause huge economic losses for farmers. Coupled with the relatively high lignin content, which impedes the degradation of carbohydrate in rumen, alfalfa has unbalanced and asynchronous degradation ratio of nitrogen to carbohydrate (N/CHO) in rumen. Genetic engineering approaches have been used to manipulate the expression of genes involved in important metabolic pathways for the purpose of improving the nutritive value, forage yield, and the ability to resist abiotic stress. Such gene modification could bring molecular structural changes in alfalfa that are detectable by advanced structural analytical techniques. These structural analyses have been employed in assessing alfalfa forage characteristics, allowing for rapid, convenient and cost-effective analysis of alfalfa forage quality. In this article, we review two major obstacles facing alfalfa utilization, namely poor protein utilization and relatively high lignin content, and highlight genetic studies that were performed to overcome these drawbacks, as well as to introduce other improvements to alfalfa quality. We also review the use of advanced molecular structural analysis in the assessment of alfalfa forage for its potential usage in quality selection in alfalfa breeding.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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) | 0.000 | 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".