Digital Mapping of Soil Drainage Classes Using Multitemporal RADARSAT-1 and ASTER Images and Soil Survey Data
Why this work is in the frame
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Bibliographic record
Abstract
Discriminant analysis classification (DAC) and decision tree classifiers (DTC) were used for digital mapping of soil drainage in the Bras-d’Henri watershed (QC, Canada) using earth observation data (RADARSAT-1 and ASTER) and soil survey dataset. Firstly, a forward stepwise selection was applied to each land use type identified by ASTER image in order to derive an optimal subset of soil drainage class predictors. The classification models were then applied to these subsets for each land use and merged to obtain a digital soil drainage map for the whole watershed. The DTC method provided better classification accuracies (29 to 92%) than the DAC method (33 to 79%) according to the land use type. A similarity measure (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>S</mml:mi></mml:math>) was used to compare the best digital soil drainage map (DTC) to the conventional soil drainage map. Medium to high similarities (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mn>0.6</mml:mn><mml:mo>≤</mml:mo><mml:mi>S</mml:mi><mml:mo><</mml:mo><mml:mn>0.9</mml:mn></mml:math>) were observed for 83% (187 km 2 ) of the study area while 3% of the study area showed very good agreement (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>S</mml:mi><mml:mo>≥</mml:mo><mml:mn>0.9</mml:mn></mml:math>). Few soil polygons showed very weak similarities (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>S</mml:mi><mml:mo><</mml:mo><mml:mn>0</mml:mn><mml:mo>.</mml:mo><mml:mn>3</mml:mn></mml:math>). This study demonstrates the efficiency of combining radar and optical remote sensing data with a representative soil dataset for producing digital maps of soil drainage.
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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 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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