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).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 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 | 0.001 | 0.001 |
| 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 | 0.000 |
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 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".