Seasonal and spatial variability of surface hydraulic properties
Bibliographic record
Abstract
Monitoring, using remote sensing, of seasonal changes of land surface properties such as: freezing and thawing of wetland, freezing and thawing of soil, as well as soil moisture and snow cover mapping, relies on capabilities to relate physical characteristics of the study area and satellite observations. The water and ice content of soil are very important parameters for determination of the storage capacity and hydraulic properties of the surface. Thus, the objective of this work was to derive indices that would provide link between remote sensing observations and regional/seasonal variability of surface properties. Radiative transfer and radar backscatter models were configured to simulate and analyse the link between surface properties, atmospheric conditions, and the signals measured by active and passive sensors. In this work we present the results of model simulation of microwave backscattering for different land surface types e.g., wetland, forest cover. Several different study sites were selected in Ontario. RADARSAT (Fall, Winter, and Spring) images, at different resolution, were acquired for several different sites in Northern and Southwest Ontario. Two independent transitions corresponding to soil thaw and possible canopy thaw were revealed by the data. Analysis of Radarsat scenes indicates shifts in radar backscatter related to varying environmental conditions during image acquisition, e.g.,snow storm. Consequences of such effects on the analysis of surface characteristics can be significant.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".