Characterization of nitrogen and water status in oat leaves using optical sensing approach
Bibliographic record
Abstract
BACKGROUND: Optical sensing is a potential tool to estimate plant N status, but soil water deficits may interefere with forming a clear relationship. A greenhouse study was conducted with oat plants treated with three water regimes and four N levels to determine whether optical sensing could be used to estimate leaf N and relative water content (RWC). RESULTS: Leaf N was strongly correlated with reflectance at 550 nm and at around 705 nm, and N treatments caused a red-edge peak shift to lower wavelength. The ratio of the first derivative reflectance at 741-696 nm (FDRE) was identified to be a good estimator of leaf N at jointing (R(2) = 0.90) and heading (R(2) = 0.86) stages across water treatments. Leaf N also had a stronger association with the red-edge position (REP) at both stages (R(2) = 0.83 and 0.78), or with the ratio R4 (R760/R550) at jointing (R(2) = 0.88), than with chlorophyll meter (SPAD) readings. Under water stress, the predictive accuracy of leaf N increased with these reflectance indices, but decreased using SPAD readings. CONCLUSION: The results indicate that specific reflectance indices of FDRE, REP and R4 may be used for a rapid and non-destructive estimation of oat plant N status over a range of water regimes.
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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".