GIS Spatial-Temporal Modeling of Water Systems in Greater Toronto Area, Canada
Bibliographic record
Abstract
Modeling landscape with high-resolution digital elevation model (DEM) in a geographic information system can provide essential morphological and structural information for modeling surface processes such as geomorphologic process and water systems. This paper introduces several DEM-based analysis processes applied to characterize distribution and their interactions of ground and surface water systems in the Greater Toronto Area (GTA), Canada. The stream networks and drainage basin systems were derived from the DEM with 30 m resolution and the regularities of the surface stream and drainage patterns were modeled from a statistical/multifractal point of view. Together with the elevation and slope of topography, other attributes defined from modeling the stream system, and drainage networks were used to associate geological, hydrological and topographical features to water flow in river systems and the locations of artesian aquifers in the study area. Stream flow data derived from daily flow measurements recorded at river gauging stations for multi-year period were decomposed into drainage-area dependent and drainage-area flow components by two-step frequency and spatial analysis processes. The latter component was further demonstrated to relate most likely to the ground water discharge. An independent analysis was conducted to model the distribution of aquifers with information derived from the records of water wells. The focus was given on quantification of the likelihood of ground water discharge to river and ponds through flowing wells, springs and seepages. It has been shown that the Oak Ridges Moraine (ORM) is a unique glacial deposit that serves as a recharge layer and that the aquifers in the ORM underlain by Hilton Tills and later deposits exposed near the steep slope zone of the ridges of ORM provide significant discharge to the surface water systems (river flow and ponds) through flowing wells, springs and seepages. Various statistics (cross- and auto-correlatio
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".