Prediction of soil nutrient regime based on a model of DEM-generated clay content for the province of Nova Scotia, Canada
Bibliographic record
Abstract
Zhao, Z., Ashraf, M. I., Keys, K. S. and Meng, F-R. 2013. Prediction of soil nutrient regime based on a model of DEM-generated clay content for the province of Nova Scotia, Canada. Can. J. Soil Sci. 93: 193–203. Soil nutrient regime (SNR) maps are widely required by ecological studies as well as forest growth and yield assessment. Traditionally, SNR is assessed in the field using vegetation indicators, topography and soil properties. However, field assessments are expensive, time consuming and not suitable for producing high-resolution SNR maps over a large area. The objective of this research was to develop a new model for producing high-resolution SNR maps over a large area (in this case, the province of Nova Scotia). The model used 10-m resolution clay content maps generated from digital elevation model data to capture local SNR variability (associated with topography) along with coarse-resolution soil maps to capture regional SNR variability (associated with differences in landform/parent material types). Field data from 1385 forest plots were used to calibrate the model and another 125 independent plots were used for model validation. Results showed field-identified SNRs were positively correlated with predicted clay content, with some variability associated with different landform/parent material types. Accuracy assessment showed that 63.7% of model-predicted SNRs were the same as field assessment, with 96.5% within ±1 class compared with field-identified SNRs. The predicted high-resolution SNR map was also able to capture the influence of topography on SNR which was not possible when predicting SNR from coarse-resolution soil maps alone.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".