Accelerated Aging Test to Determine the Vigor of Mungbean Seeds
Bibliographic record
Abstract
The use of good quality seeds is fundamental for proper establishment of a crop. In this way, for more precise determination of seed quality, vigor tests are performed in addition to the germination test. These tests enable the selection of the best lots for commercialization and planting. This study evaluates the effects of temperature and exposure times of the accelerated aging test for vigor classification of mungbean seed lots. Seeds of the mungbean cultivar Esmeralda were used, being obtained from four cultivated plots at the State University of Goiás (UEG), Ipameri Câmpus, in the 2013, 2015, 2016, and 2017 harvests. The lots were initially characterized using germination and vigor tests (first germination count, germination rate index, seedling length, and seedling fresh and dry weight). The accelerated aging test was conducted with a completely randomized experimental design, in a 2 × 4 factorial arrangement consisting of two temperatures (40 and 42 °C) and four times (24, 48, 72, and 96 hours), with four replicates of 50 seeds per lot. The lots showed significant differences in the germination test, first germination count, germination rate index, and seedling length. The accelerated aging test was efficient in classifying lots. Lot 3 obtained the best results, while lot 1 obtained the lowest ones. The combination of 42 °C temperature and 72 h of seed exposure to the accelerated aging test is the best to classify mungbean seed lots.
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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 | 0.001 | 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".