Leaf Spectral Reflectance of Maize Seedlings and Its Relationship to Cold Tolerance
Bibliographic record
Abstract
Increasing early‐season cold tolerance of maize ( Zea mays L.) has the potential to lengthen its growing season, reduce its environmental impact, and enhance its yields. Cold‐ and warm‐grown plants differ for biomass accumulation and spectral reflectance, the latter caused by differences in leaf chlorophyll content, carotenoid content, or other chemical and morphological attributes. Here, we evaluate genetic leaf spectral reflectance diversity across 38 inbred and 14 hybrid maize genotypes grown in cold and control temperatures. Genotypes varied for spectral reflectance indices correlated with chlorophyll content and for an index correlated with the ratio of leaf carotenoids to chlorophylls. Genotypic differences greatly contributed to spectral reflectance variation across all visible wavelengths, with the greatest genetic variation between 500 and 600 nm and around 700 nm. Cold treatment effects were most significant across the same wavelengths. Spectral indices indicated lower chlorophyll and a higher carotenoid/chlorophyll level in cold‐exposed plants relative to control plants. Genotype × temperature interactions were small relative to genotype effects. Cold tolerance, measured as the ratio of dry matter accumulation in cold‐treated plants relative to control plants, varied among hybrids and inbreds. Correlations of cold tolerance with known spectral reflectance indices, reflectance first derivatives, and novel, normalized spectral indices were mostly low and occurred only in certain germplasm. One reflectance parameter, the reflectance curve slope at 828 nm, was consistently related to genotypic cold tolerance, even when measured on plants that had not been exposed to cold.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".