Interaction of Mechanical Damage and Chemical Treatment and Its Effects on Soybean Seed Physiological Quality
Bibliographic record
Abstract
The objective was to evaluate the effects of chemical treatment and levels of mechanical damage and the lignin content of seed coat on soybean seed physiological quality. Two soybean cultivars were used: BMX Lança (58I60 RSF IPRO) and BMX Zeus (55I57 RSF IPRO), with different levels of mechanical injury identified by the tetrazolium test. The chemical treatments used were: control; Carbendazim + Thiram; Carbendazim + Thiram + Dry Powder; Imidacloprid + Thiodicarb; Imidacloprid + Thiodicarb + Dry Powder. A completely randomized design was used, in an 8 × 5 factorial scheme (Types of Samples × Seed Treatment). Physiological quality was evaluated by germination, primary root length, seedling dry mass, accelerated aging and seedling emergence tests. Also, the lignin content in seed coat, one-thousand-seed weight and uniformity test were performed. Data were submitted to analysis of variance (F test) and the mean comparison by the Tukey’s test (p < 0.05). Cultivars showed differences in the tegument lignin content. The treatment with Imidacloprid + Thiodicarb + dry powder promoted greater reduction in seed physiological potential, intensifying in seeds with more severe damage levels. The lignin content in soybeans seed coat influences the occurrence of mechanical injuries. Seeds with greater intensities of mechanical damage are more susceptible to phytotoxic effects promoted by chemical treatment, since such effects are intensified with the incorporation of dry powder in the seed treatment.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".