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
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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