Near-infrared spectroscopy for soil water determination in small soil volumes
Bibliographic record
Abstract
Critical soil water levels for soil microscale processes are difficult to determine because of variability in large soil volumes and lack of techniques for logging soil water contents in small soil volumes. This study tested nearinfrared (NIR) spectroscopy for soil water content determination. Five soil horizons with a range in soil texture, soil organic carbon, carbonates, pH and horizon depth, were tested at air-dry, field capacity and 0.1 MPa tension water content. Volumetric soil water content, determined using the standard method of oven-drying and soil bulk density, was compared to NIR absorbance in various combinations and wavelengths. The NIR spectra obtained with the probe in direct contact with the soil gave better results than when the probe was separated from the soil with a glass slide. The most reliable validation results were obtained using a multivariate partial least squares regression of the full spectrum with an r2 of 0.95 and RMSE of prediction of 6.4%. Smoothing and derivatives of the spectra did not improve the validation results. The relationships for absorbance at single wavelength segments, ratios, differences and area under the curve around the 1940 nm peak were good (r2 values near 0.85 ) but poorer than the results using the full spectra. The high correlation coefficients obtained with the wide variety of soils utilized in this study suggest that NIR absorbance is a practical method for determining volumetric soil water content for small soil volumes. Key words: Near-infrared spectroscopy, soil water, Near-infrared absorbance
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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 | 0.001 | 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 | 0.000 | 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) | 0.002 | 0.001 |
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".