Prediction of Nuclear Magnetic Resonance Carbon Fractions in Decomposing Forest Litter Using Diffuse Reflectance Infrared Fourier Transform Spectroscopy and Partial Least Squares Regression
Bibliographic record
Abstract
A diffuse reflectance infrared Fourier transform (DRIFT) spectroscopy method was developed to enable DRIFT to be used as a substitute for 13C-nuclear magnetic resonance (13C-NMR) spectroscopy in predicting specific functional groups containing carbon. As part of the Canadian Intersite Decomposition Study, samples of 10 foliar litter types (trembling aspen, American beech, bracken fern, black spruce, Douglas-fir, plains rough fescue, jack pine, tamarack, white birch, western redcedar) and one wood type (western hemlock) at one site and a subset of three foliar litters (trembling aspen, black spruce, plains rough fescue) at three other colder sites undergoing field exposure for 12 years were annually collected. The DRIFT spectra were collected for all samples, with a subset of litter samples also analyzed by 13C-NMR spectroscopy with cross-polarization and magic-angle spinning. Partial least squares calibrations were calculated from the DRIFT spectra for the seven NMR regions representing specific carbon-containing functional groups. These calibrations were then used to predict the proportion of each NMR region in each sample. A single nondestructive sampling using as little as 0.5 g of sample gave measurements for all of the NMR regions. The DRIFT was demonstrated as a fast and simple analysis method for analyzing large numbers of samples to give fair estimates of the NMR regions for each litter type at all four sites.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".