Fast and Non-Destructive Determination of N, P, and K in Sorghum, Oat, and Corn Residue Using Near-Infrared Spectroscopy
Bibliographic record
Abstract
A fast and non-destructive method is reported to nitrogen, phosphorus and potassium quantification in sorghum, oat, and maize residues. The reflectance spectra of 261 litter plant samples using near-infrared spectroscopy were obtained with integrating sphere and sampling rotator. Second derivative spectra and Partial Least Squares were used to develop calibration and validation models. The cross-validation (leave-one-out) technique was used to evaluate the performance of the calibration and validation models, based on analytical parameters, root-mean-square error of estimation, determination coefficient, number of latent variables, residual prediction deviation, root-mean-square error of cross-validation. It was concluded that near-infrared spectroscopy and chemometric tools are a fast and non-destructive alternative to determine nitrogen and phosphorus content in sorghum, oat, and maize residues using calibration and validation models developed according to values obtained from traditional chemical methods. For potassium content, the results indicate the low quality (imprecision) of the calibration and validation models.
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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) | 0.000 | 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 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".