Spiking regional vis-NIR calibration models with local samples to predict soil organic carbon in two High Arctic polar deserts using a vis-NIR probe
Bibliographic record
Abstract
Guy, A. L., Siciliano, S. D. and Lamb, E. G. 2015. Spiking regional vis-NIR calibration models with local samples to predict soil organic carbon in two High Arctic polar deserts using a vis-NIR probe. Can. J. Soil Sci. 95: 237-249. In situ visible and near-infrared (vis-NIR) spectroscopy is a potential solution to the logistic constraints limiting the accuracy and spatial resolution of soil organic carbon (SOC) estimates for Arctic regions. The objective of our study was to develop a calibration model based on field-condition soils for in situ applications to predict SOC in High Arctic polar desert soils from vis-NIR spectra. Soils (n=240) for calibration models were collected from three regional Canadian Arctic sites in 2010 and two local target sites in 2013. Local and regional calibration models were developed using partial least squares regression (PLSR). We assessed whether spiking or spiking and extra-weighting, regional models with calibration samples from local sites improved prediction of the local sites. The local model yielded successful prediction of target sites (R2=0.91) whereas unspiked regional models had poor prediction accuracy (R2=0.07 to 0.36; n=4). Spiking regional models with as few as 12 local samples greatly improved the SOC prediction of target sites; the best spiked models had R2 between 0.69 and 0.86. Extra-weighting spiking subsets in regional models yielded limited improvements in prediction performance. These results suggest that regional vis-NIR calibration models can be successfully used to predict SOC in High Arctic polar desert soils. The in situ application of these calibration models using field-portable instruments in remote areas, relative to traditional laboratory methods, can achieve higher sample sizes and the ability to characterize the spatial variability of SOC.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".