Evaluating water quantity and quality of Canadian Great Lakes Watershed using LTHIA GIS Model.
Bibliographic record
Abstract
The Great Lakes, also known as the Great Lakes of North America, are a series of interconnected freshwater lakes located in the upper mid-east region of North America located at the border of Canada and the United States of America (USA). The Great Lakes are a source of drinking water for 10% of Americans and 25% of Canadians. Human activities have significantly degraded the Great Lakes in the past few decades. Against this backdrop, conducting a detailed study to assess the water quality and its quantification in the Canadian Great Lakes Watershed (CGLW) seems imperative. This study used the LTHIA model to analyze the surface runoff and two Non-Point Source pollution – total suspended solids (TSS) and total phosphorus (TP) of the Canadian Great Lakes watershed. The temporal analysis showed the highest runoff, TSS and TP in the Northern Lake Erie sub-watershed in 1954. In contrast, the lowest was observed in the Northwestern Lake Superior sub-watershed in 1952. The spatial analysis showed higher runoff, TSS and TP in the Eastern Lake Huron and Northern Lake Erie sub-watersheds. The decadal analysis revealed higher runoff, TSS and TP in 1980-90, 1990-99 and 2000-09. The climate change analysis revealed more variation in the runoff, TSS, and TP were projected in mid-century (2035-64) compared to end-century (2070-99). Finally, it has been shown that the LTHIA model can successfully simulate both water quantity and quality-related processes and climate change effects.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".