Relationship Between Lignin Metabolism and Lodging Resistance of Culm in Buckwheat
Bibliographic record
Abstract
To disclose the relationship between lignin content and lodging resistance, field experiments were conducted in 2012 and 2013 to investigate the dynamic changes of lignin content, the activities of four enzymes involved in lignin metabolism in culm, as well as the snapping resistance and lodging index of buckwheat. The lignin content and activities of phenylalanine ammonia-lyase (PAL), tyrosine ammonia-lyase (TAL), cinnamyl alcohol dehydrogenase (CAD), and 4-coumarate: CoA ligase (4CL) were tested with the 2nd internode of four cultivars with different lodging resistance. The snapping resistance and culm lodging index were determined at anthesis, grain filling, and maturity stages. The lignin content varied significantly among cultivars. The XD cultivar, which showed high resistance to culm snapping and lodging, had higher lignin content and higher activity of PAL, TAL, 4CL, and CAD than other cultivars. The lignin content was significantly negatively correlated with the actual lodging percent (r = -0.844, P < 0.01) and the lodging index (r = -0.832, P < 0.05), and was significantly positively correlated with the snapping resistance (r = 0.873, P < 0.01). The activities of PAL, TAL, 4CL, and CAD were positively correlated with lignin content with correlation coefficients of 0.984 (P<0.01), 0.619 (P > 0.05), 0.927 (P < 0.01), and 0.862 (P < 0.01), respectively. In conclusion, our results indicate that the activities of PAL and 4CL were the key enzymes that influenced the lignin content, and that the lignin content can be used as main indicators to evaluate the lodging resistance of buckwheat.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".