Evaluating water quantity and quality of Canadian Great Lakes Watershed using LTHIA GIS Model.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 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) | 0.000 | 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 it