Applicability of Diffuse Reflectance Fourier Transform Infrared Spectroscopy to the Chemical Analysis of Decomposing Foliar Litter in Canadian Forests
Bibliographic record
Abstract
Diffuse reflectance Fourier transform infrared (DRIFT) spectroscopy was used to compare changes in organic chemistry of 10 species of foliar litter undergoing in situ decomposition for 1 to 12 years at four forested sites representing a range of climates in Canada. Three types of foliar litter (conifer, black spruce; deciduous, trembling aspen; and a grass, fescue) were studied on all four sites plus seven additional types (Douglas, fir; western red cedar; white birch; jack pine; beech; bracken fern; and tamarack) studied at the warmest site (Morgan Arboretum [MAR]). For all litter samples, DRIFT spectra were collected, and carbon and N were contents determined. A subset of samples (10 types × 5 years for MAR, three types × 5 years for the other sites) was analyzed by classical chemical methods for proximate fractions. Spectra for subsets of chemically analyzed samples from MAR were used to prepare partial least squares calibration equations for each chemical variable. These calibrations were then used to predict chemical concentrations for samples in a reserved subset, in intervening years, and from the three other sites, and then validated against measured values. Results indicated a trend of decline in proportion of nonpolar and water-soluble extractables with an increase in proportion of acid unhydrolyzable residue. The DRIFT was demonstrated as a fast and simple analysis method for analyzing large numbers of samples to give good estimates of litter chemistry. A single nondestructive sampling using as little as 0.1 g of sample gave reasonable values of carbon, N, and proximate fractions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".