Determination of organic carbon and nitrogen in particulate organic matter and particle size fractions of Brookston clay loam soil using 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
The objective of this study was to determine whether models developed from infrared spectroscopy could be used to estimate organic carbon (C) content, total nitrogen (N) content and the C:N ratio in the particulate organic matter (POM) and particle size fraction samples of Brookston clay loam. The POM model was developed with 165 samples, and the particle size fraction models were developed using 221 samples. Soil organic C and total N contents in the POM and particle size fractions (sand, 2000–53 µm; silt, 53–2 µm; clay, <2 µm) were determined by using dry combustion techniques. The bulk soil samples were scanned from 4000 to 400 cm −1 for mid‐infrared (MIR) spectra and from 8000 to 4000 cm −1 for near‐infrared (NIR) spectra. Partial least squares regression (PLSR) analysis and the ‘leave‐one‐out' cross‐validation procedure were used for the model calibration and validation. Organic C and N content and C:N ratio in the POM were well predicted with both MIR‐ and NIR‐PLSR models ( = 0.84–0.92; = 0.78–0.87). The predictions of organic C content in soil particle size fractions were also very good for the model calibration ( = 0.84–0.94 for MIR and = 0.86–0.92 for NIR) and model validation ( = 0.79–0.94 for MIR and = 0.84–0.91 for NIR). The prediction of MIR‐ and NIR‐PLSR models for the N content and the C:N ratio in the sand and clay fractions was also satisfactory ( = 0.73–0.88; = 0.67–0.85). However, the predictions for the N content and C:N ratio in the silt fraction were poor ( = 0.23–0.55; = 0.20–0.40). The results indicate that both MIR and NIR methods can be used as alternative methods for estimating organic C and total N in the POM and particle size fractions of soil samples. However, the NIR model is better for estimating organic C and N in POM and sand fractions than the MIR model, whereas the MIR model is superior to the NIR model for estimating organic C in silt and clay fractions and N in clay fractions.
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 | 0.001 | 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 | 0.000 |
| 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 it