Efficacy of different indices derived from spectral reflectance of wheat for nitrogen stress detection
Why this work is in the frame
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Bibliographic record
Abstract
Abstract The productivity of cereal crops is mainly related to their nitrogen status. It is hypothesized that the spectral reflectance data could be used to predict wheat nitrogen status with spectral indices and that their performance depends on the nature of the interaction of the solar radiation with the crop canopy. A wheat crop was raised with 12 levels of nitrogen treatments: 0, 15, 30, 40, 50, 60, 70, 80, 90, 100, 110, and 120 kg ha−1, with uniform phosphorous and potassium nutrition and uniform water and management practice. The spectral reflectance measurements of the crop canopy were taken at 5 nm intervals, throughout the crop growth period. Different spectral indices, both broadband (ratio as well as orthogonal) and hyperspectral indices were computed throughout the growing season. Canopy Colour Difference (ΔE), an index developed from the entire visible region and hence broader than the spectral indices developed hitherto, was also estimated from the reflectance data. Simple linear relationships developed between spectral indices versus applied nitrogen levels as well as the plant nitrogen content revealed that the hyperspectral indices are less sensitive in comparison to broadband indices. The result was reinforced by a higher correlation between the colour difference, NDVI and Greenness Index with plant nitrogen level/content, as opposed to hyperspectral indices.
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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 | 0.000 |
| 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) | 0.000 | 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 it