Indirect Estimations of Lentil Leaf and Plant N by SPAD Chlorophyll Meter
Bibliographic record
Abstract
A Soil Plant Analysis Development (SPAD) chlorophyll meter can be used to screen for leaf nitrogen (N) concentration in breeding programs. Lentil ( Lens culinaris L.) cultivars were grown under varied N regimes, SPAD chlorophyll meter readings (SCMR) were recorded from the cultivars leaves, and leaf N concentration was measured by combustion. Linear regression and the nonlinear Radial Basis Functions (RBF) neural networks models were employed to estimate leaf N concentration (LNC) based on the SCMR values. The closest estimates of LNC were obtained from the multivariate models in which the combination of plant age, leaf thickness, and SCMR was employed as the independent variable. In comparison, SCMR as the single independent variable in both models estimated less than 50% of LNC variations. The results showed significant effects of soil moisture and plant age on the association of LNC –SCMR as well as the relationship of LNC with plant N, grain yield, and days to maturity. However, the effect of cultivar on the measured variables was negligible. Although lentil N can be diagnosed by comparing SCMR values of the crop with those from a well-fertilized (N fixing) plot, the results did not support using SPAD chlorophyll meter for screening lentil LNC.
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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 | 0.000 |
| 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 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".