Improved parent material map disaggregation methods in the Saskatchewan prairies using historical bare soil composite imagery
Bibliographic record
Abstract
The major drivers of soil variation in Saskatchewan at scales finer than the existing soil maps are parent material variance, slope position, and salinity. There is therefore a need to generate finer-scale parent material maps as part of updating soil maps in Saskatchewan. As spatially referenced soil point data are lacking in Saskatchewan, predictive soil mapping methods that disaggregate existing soil parent material maps are required. This study focused on investigating important environmental covariates to use in parent material disaggregation, particularly bare soil composite imagery (BSCI). Synthetic point observations were generated using an area-proportional approach based on existing soil survey polygons and a random forest model was trained with those synthetic observations to predict parent material classes. Including BSCI as environmental covariates increased model accuracy from 0.38 to 0.52 and the model Kappa score from 0.19 to 0.35 compared with models where it was not included. Models that included training points from all locations, regardless of whether BSCI was available, and included BSCI as environmental covariates had similar results to the BSCI model with an accuracy of 0.48 and a Kappa value of 0.30. Based on these results, BSCI is an important covariate for parent material disaggregation in the Saskatchewan Prairies. Future work to disaggregate soil classes based on slope position and salinity, and to combine those methods with parent material disaggregation is needed to generate detailed soil maps for the Canadian Prairies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".