Combustion and Spectral Methods for Quantifying Carbon and Nitrogen Concentrations in Pacific Northwest Douglas-Fir Forest Soils
Bibliographic record
Abstract
Traditional combustion methods for assessing soil carbon (C) and nitrogen (N) stocks are time consuming and expensive; visible and near-infrared (VNIR) methods offer a quick and inexpensive alternative for establishing soil C and N concentrations. We compared combustion and spectral methods for quantifying soil carbon and nitrogen concentrations. We sampled organic and mineral soil horizons in managed and old-growth Douglas-fir (Pseudotsuga menziesii) forests in western Oregon. We applied combustion methods to determine total soil carbon and nitrogen concentrations of these samples. We then applied VNIR methods to derive a reference spectral library for analyzing Pacific Northwest (PNW) andesitic forest soils. Our spectral analysis confirmed that visible range spectra (especially in the 650-750 nm range) are the most useful for distinguishing differences in sample soil organic matter content. Our results provide a robust calibration model for applying spectral analysis combined with Partial Least Square Regression (PLSR) to quantify carbon and nitrogen stocks in PNW Douglas-fir forest soils. Model validation resulted in R2 values ranging from 0.92 to 0.95 for C and from 0.73 to 0.84 for N.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".