Soybean Industrial Seed Treatment: Effect on Physiological Quality During Storage
Bibliographic record
Abstract
The aim of this study was to analyze soybean seed physiological quality after being subjected to various mixtures of pesticides via industrial seed treatment. The experiment was performed at the seed laboratory of the company BioGrow, located at São Paulo-SP, using soybean seeds cultivar NS 6700 IPRO which were subjected to 11 different treatments. Seed treatment was carried out using a treater Momesso, model L5-K, calibrated to apply a spray volume of 0.5 L 100 kg-1 of seeds in which the volume of each treatment was adjusted using distilled water. After treatment, seeds were spread over plastic strays for drying for a period of 24 hours under environmental conditions. Once dry, seeds were packed in paper bags and stored for 0 (control), 45, 90, 135 and 180 days, under uncontrolled conditions of temperature and relative humidity, when seed physiological quality was evaluated using the following tests: germination, accelerated aging, seedling emergence, speed of emergence index and speed of emergence. Soybean industrial seed treatment before storage for up to 180 days is practicable using the mixtures of pesticides tested for storing seeds under environmental conditions. All treatments tested contribute to the maintenance of seed quality throughout storage.
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 | 0.000 | 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) | 0.001 | 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".