Quantitative reflectance spectroscopy as an alternative to traditional wet lab analysis of foliar chemistry: near-infrared and mid-infrared calibrations compared
Bibliographic record
Abstract
Quantitative reflectance spectroscopy offers an alternative to traditional analytical methods for the determination of the chemical composition of a sample. The objective of this study was to develop a set of spectroscopic calibrations to determine the chemical composition (nutrients, carbon, and fiber constituents, determined using standard wet lab methods) of dried conifer foliage samples (N = 72), and to compare the predictive ability of calibrations based on three different spectral regions: visible and shortwave near infrared (VISsNIR, 400- to 1100-nm wavelengths), near infrared (NIR, 1100- to 2500-nm wavelengths), and mid infrared (MIR, 2500- to 25 000-nm wavelengths). To date, most quantitative reflectance spectroscopy has been based on the VISsNIRNIR, and the ability of MIR calibrations to predict the composition of tree foliage has not been tested. VISsNIR calibrations were clearly inferior to those based on longer wavelengths. For 8 of 11 analytes, the MIR calibrations had the lowest standard error of cross-validation, but in most cases the difference in accuracy between NIR and MIR calibrations was small, and against an independent validation set, there was no clear evidence that either spectral region was superior. Although quantitative MIR spectroscopy is at a more primitive state of development than NIR spectroscopy, these results demonstrate that the mid infrared has considerable promise for quantitative analytical work.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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) | 0.001 | 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 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".