Semi-automated Phenotyping of Soybean Seedlings and Its Relation With Physiological Seed Quality
Bibliographic record
Abstract
The development of procedures enabling agility and effectiveness to the analyses of seed vigor are great advances for the seed research field. The aim of this paper was to evaluate the efficiency of the Seedling Analysis System (SAPL®) to seedling phenotyping and determining the physiological potential of soybean seeds, in comparison with the information provided by traditional vigor tests recommended for this species. The characterizing of physiological potential of the seed lots was carried out based on germination tests, first germination count, seedling emergence, cold test, and electrical conductivity. The results of these tests were compared with the data obtained using image analysis with the software SAPL®, which was used to evaluate the development seven- day old seedlings. It was obtained the length of shoot, primary root and entire seedling, growth rates, uniformity, vigor index and corrected vigor index, based on different variations of the growth/uniformity ratio, namely 80:20, 70:30, 60:40, in the vigor index calculation. Analysis of images of the seven-day old seedlings using SAPL® enables phenotyping of soybean seedlings and is a consistent and promising alternative for identifying differences in vigor between soybean seed lots, equivalent to that found with traditional vigor tests.
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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 | 0.001 |
| 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".