Performance of the Global-Local modelling approach for FT-NIR predictions of SOC and TN in diverse Saskatchewan agricultural soils
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
• Global-Local models performed better than Global for predicting SOC and TN. • Site-Specific models outperformed all other models evaluated. • Global-Local model performed similar to the best Lab or Neighbour model. • Spiking performed as well as or better than the Global-Local model, depending on the site. • Inclusion of site-specific samples improved model performance. Precision agriculture requires a reliable, cost-effective method to measure soil organic carbon (SOC) and total nitrogen (TN), and Fourier Transform Near Infrared (FT-NIR) spectroscopy offers a promising solution. Here, we applied the Global-Local model to improve FT-NIR SOC and TN predictions in Saskatchewan agricultural soils. Soil samples (SOC: n = 1876; TN: n = 1442) were collected in 2020 and 2021 from six Saskatchewan agricultural regions. Spectral data were acquired, preprocessed using continuous wavelet transform (CWT), and modelled using Cubist regression. The Global-Local model was applied by combining a small subset of site-specific samples ( Lab ) with their k -nearest neighbours ( Neighbour ) from Saskatchewan spectral datasets. Its performance was compared with Leave-One-Site-Out ( LOSOV ), site-specific, Lab , Neighbour , and traditional spiking. Compared to LOSOV (SOC: R 2 = 0.55 – 0.76, CCC = 0.67 – 0.79, RPD = 1.20 – 1.44), site-specific models gave higher performance (SOC: R 2 = 0.71 – 0.88, CCC = 0.82 – 0.92; RPD = 1.59 – 2.70). The Global-Local model performed better than LOSOV and performed similarly to the best Lab or Neighbour models. Compared to the Global-Local , traditional spiking either improved or gave similar results due to higher variability in target variable and spectra datasets. The more accurate models using either spiking or Global-Local than LOSOV confirms the importance of incorporating site-specific samples into training datasets. Our results indicate that the application of the Global-Local model should be restricted to an individual field level, which was its original purpose. Future studies on optimization of the Global-Local model is needed to scale-up its application.
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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 | 0.000 |
| 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