A landscape approach to evaluate sources of nutrient and sediment to the Nottawasaga River, a tributary of Georgian Bay, Lake Huron
Bibliographic record
Abstract
The overall goal of this thesis is to present a comprehensive understanding of the Nottawasaga River system. In the first chapter, we will examine how landscape features (geomorphology and land cover) drive spatial variation in nutrient and sediment loading from 11 sub-watersheds to the Nottawasaga River. The second chapter will relate how tributary loading and other in-stream processes (riffles, substrate, dissolved oxygen) contribute to the longitudinal variation in water quality along with middle and lower reaches of the Nottawasaga River. Finally, in the last chapter we use 13 water quality variables to develop a Stream Water Quality Index (SWQI) to identify critical areas in the NRW that are most at risk. This thesis will provide environmental agencies with useful information to help implement management strategies to improve the health of riverine systems at a watershed scale.
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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 | 0.000 | 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) | 0.018 | 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".