Lignin and Activity Enzymatic in Susceptibility to Weathering Damage on Soybean Seeds
Bibliographic record
Abstract
The aim of this study was to evaluate the isoenzyme activity in soybean seeds with different lignin contents subjected to harvest delay with artificial incidence of rainfall before and after storage. The experiment was conducted in a randomized block design with three replicates and a 5 × 3 × 2 factorial design, being five soybean cultivars, three harvest seasons (R8, R8 + one pre-harvest rainfall simulation, and R8 + two pre-harvest rainfall simulations), and two storage seasons (0 and 180 days). The pre-harvest rainfall simulations were performed through irrigation at the intensity of 30 mm of water until the pods were soaked and then collected after reached 18% water content. Seeds were evaluated regarding chemical composition (lignin content), physiological quality (germination, accelerated aging), and enzymatic activity (catalase, esterase, alcohol dehydrogenase, malate dehydrogenase, and isocitratelyase). The cultivar AS 7307 RR showed higher lignin content in the integument and higher physiological quality. The harvest delay and the artificial incidence of rainfallpromotesvariation in the electrophoretic pattern of the enzymes catalase, esterase, alcohol dehydrogenase; malate dehydrogenase, and isocitratelyase for stored and non-stored seeds.
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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".