Determination of soil organic carbon and nitrogen at the field level using near-infrared spectroscopy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study explored the use of near-infrared spectroscopy (NIRS) for the rapid analysis of organic C (C org ) and organic N (N org ) in the A horizon of soil within a single field. Soil was sampled throughout a field in Manitoba, Canada to capture soil variability associated with topography. The soil samples were oven-dried and treated with acid to remove carbonates, after which C and N were determined by dry combustion. In this study, portions of the dried soil samples not treated with acid were scanned with a near-infrared scanning spectrophotometer between 1100 and 2500 nm. Correlating the spectral and the chemical analytical data using multiple linear regression or principal component analysis/partial least squares regression gave useful correlations for C org . Over the range of 0–40 mg g -1 C org , NIR-predicted values explained 75–78% of the variance in the chemical results. Results were improved to 80% for calibrations developed for the 0–20 mg g -1 organic C range. Useful results were not obtained for N org although the literature shows that total N in soil is predictable using NIRS. It is likely that the acid treatment altered the composition of the samples in an inconsistent manner such that the chemically analyzed samples and those scanned by NIRS were different from each other in N org concentration or composition. Extrapolation of these C org results to the landscape scale implies that NIRS has potential to be a suitable method for mapping C for the purposes of monitoring C sequestration. Key words: Near-infrared spectroscopy, soil, carbon, nitrogen, topography, soil monitoring
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it