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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".