Model prediction of soil drainage classes over a large area using a limited number of field samples: A case study in the province of Nova Scotia, Canada
Bibliographic record
Abstract
Zhao, Z., Ashraf, M. I. and Meng, F.-R. 2013. Model prediction of soil drainage classes over a large area using a limited number of field samples: A case study in the province of Nova Scotia, Canada. Can. J. Soil Sci. 93: 73-83. Soil drainage maps are frequently required for crop, forest, and environmental management. However, modelling soil drainage over a large area (>1000 km2) is difficult due to complex soil-forming processes, large spatial variations, and the limited number of field samples, which are often insufficient to reflect local variations. In this study, a two-stage approach was used to produce soil drainage maps over a large area (the province of Nova Scotia). In the first stage, an existing soil drainage model developed in a small watershed with a sufficient number of field samples that could represent local topography was adopted. As a comparison, an artificial neural network model was built and calibrated with 1545 field samples across the province of Nova Scotia. Both models were used directly to predict soil drainage maps in the province of Nova Scotia. Results indicate that both models produced poor predictions. In the second stage, after dividing the entire provincial area into sub-areas (landforms) based on different division methods, corresponding linear transformation models were subsequently developed to adapt soil drainage classes produced by a base model (the existing soil drainage model) to fit field samples. Parameters of linear transformation models were estimated with field samples. Results indicate that the best linear transformation model was composed of 12 linear equations corresponding to 12 landforms (combinations of ecoregion and texture), and improved the prediction of rapidly drained (9.6%), well-drained (21.3%), moderately well-drained (14.1%), and imperfectly drained (7.5%) plots compared with the base model. Thus, the two-stage approach can obviously improve the accuracy of predicted soil drainage classes over a large area.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".