Geophysical and sedimentological assessment of urban impacts in a Lake Ontario watershed and Lagoon: Frenchman's Bay, Pickering, Ontario
Bibliographic record
Abstract
Managing the environmental impacts of urbanization on watersheds is a major problem facing Canadian communities. Meeting this challenge requires that municipal planning departments have access to good quality environmental information allowing them to develop effective land use plans and remediation policies. Managing such problems demands an interdisciplinary approach involving a range of scientific disciplines including geology, geochemistry, sedimentology, hydrogeology, hydrology, geophysics and aquatic ecology. Geoscientists from the University of Toronto and McMaster University are working with the City of Pickering, Ontario on remediation of a Lake Ontario lagoon and urbanized watershed (Frenchman's Bay) experiencing large stormwater flows and enhanced sediment erosion and transportation. Throughout the watershed, the hydrological cycle has been dramatically changed as a result of 'hardening' by roads and buildings - greatly restricting infiltration and promoting surface runoff. The urban-impacted watershed empties into the shallow, semi-enclosed coastal lagoon of Frenchman's Bay - serving as a trap for fine-grained contaminated sediment. A wide range of geophysical techniques have been employed in Frenchman's Bay lagoon to determine the geology of the lagoon, physical characteristics of bottom sediments and the distribution of contaminated sediment on its floor.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".