Curve-fitting techniques improve the mid-infrared analysis of soil organic carbon: a case study for Brookston clay loam particle-size fractions
Bibliographic record
Abstract
Abstract Few studies have evaluated structural features of soil organic carbon (SOC) in different soil particle fractions, especially SOC changes induced by tillage, using Fourier transformed mid-infrared (MIR) spectroscopy. To make a contribution in this context, soil samples of a Brookston clay loam (mesic Typic Argiaquoll) with averaged pH and organic matter concentration at 7.28 and 43.9 g kg −1 , respectively, were collected from short-term no-tillage (NT97) and mouldboard plow (CT97) treatments initiated in 1997 and long-term no-tillage (NT83) and mouldboard plow (CT83) treatments initiated in 1983 under a corn-soybean rotation, and were separated into sand, silt, and clay fractions using sonication. Structural features of SOC in these soil fractions were investigated using curve-fitting analysis of mid-infrared (MIR) spectra. Aromatic C content was found to be greater in clay- than in sand-sized fractions, while aliphatic C content was higher in sand- than in silt- and clay-sized particles. With decrease in tillage intensity, the aliphatic C gradually increased in sand- and clay-sized fractions but not in the silt-sized fraction. The aliphatic C content in sand fraction was significantly greater in NT83 than CT83 ( P < 0.05). The aromatic C in silt- and clay-sized fractions was greater in NT83 than in both CT soils, whereas aromatic C contents were higher in both CT soils than in NT83 soil. Significantly higher aromatic/aliphatic C ratio in CT83 than NT83 was found in sand-sized fractions, while the opposite trend was found in the silt-sized fraction. These findings were not apparent until the curve-fitting technique was employed, which has the capacity to quantify many overlapped bands in the spectra. This study demonstrates that the curve-fitting of MIR spectra advances the analysis of organic matter in soil samples.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".