Evaluating the utility of remotely sensed soil moisture for the characterization of runoff response over Canadian watersheds
Bibliographic record
Abstract
Remotely sensed soil moisture measurements from satellite platforms are increasingly reliable, cost-effective and widely available data sources where in situ measurements are unavailable. This research uses the Soil Moisture and Ocean Salinity mission (SMOS) satellite-derived soil moisture anomalies over a database of 65 watersheds across Canada from 2011 to 2014 to analyze the soil moisture-runoff relationship. A spatial analysis of the variability and influences on the strength of this relationship revealed that 32% of catchments showed significant (1 tailed, p < 0.05) correlations between the weekly antecedent soil moisture state of the catchment and the weekly runoff ratio. Regions of strongest correlation were related to the topographic variables of slope and elevation. These results support the use of coarse-scale satellite remote sensing as a valuable data source in hydrological studies, but recommend caution when applying the data to regions where the accuracy of satellite soil moisture data sets is less certain (such as wetlands and areas with high topography) or areas where the runoff generation mechanisms are complex (frozen soils, wetlands or prairie environments).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".