Using historic soil survey data to map water erosion hazard for land-use planning in Nova Scotia
Bibliographic record
Abstract
Keys, K., Sterling, S. M. and Guan, Y. 2015. Using historic soil survey data to map water erosion hazard for land-use planning in Nova Scotia. Can. J. Soil Sci. 95: 299-304. A fine-scale digital soil map layer for Nova Scotia was combined with historic soil survey information and digital elevation (DEM) data to generate a new water erosion hazard map based on inherent soil erodibility and slope class. Results showed approximately 62% of Nova Scotia has low erosion hazard, 33% has moderate hazard, and 5% has high hazard. The erosion hazard map was subsequently used in a provincial watershed assessment program (NSWAP) to map potential water quality stressors related to land-use patterns on erodible soils. This work is an example of how historic soil survey data can be utilized to create new information for land-use planning and environmental assessment.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".