Digital Image Analysis of Low-Temperature Responses in Sweet Corn Hybrid Seedlings
Bibliographic record
Abstract
Breeding for stress-tolerant hybrids begins with screening germplasm for tolerant genotypes. We employed a non-destructive and objective method to evaluate the low-temperature response of sweet corns at the seedling stage, using a digital image analysis. It was estimated using summed leaf area, a new parameter defined as the sum of the leaf area measured from images taken at different angles. The summed leaf area, SPAD, shoot and root fresh weight, and total root length were significantly different among hybrids. The group mean of sugary endosperm type was significantly higher than that of shrunken type for all traits but SPAD. For the summed leaf area, the top three ranked hybrids were from the sugary type, but the area for the following three hybrids from the shrunken type did not differ from that for the first three hybrids. The summed leaf area was correlated with SPAD (r = 0.49 **), shoot (r = 0.99 **) and root (r = 0.93 **) fresh weight, and total root length (r = 0.76 **). Phytoglycogen in seeds only had a significant correlation (r = 0.46 **) with the area. The summed leaf area of only one hybrid differed between normal- and low-temperature conditions. The low-temperature response based on the summed leaf area was reflected in the field condition, with a few exceptions. The results suggest that the summed leaf area via digital image analysis can be used to evaluate low-temperature response in sweet corns.
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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".