Assessment of the degradation of aesthetics Beneficial Use Impairment in the Toronto and region Area of Concern
Bibliographic record
Abstract
Toronto and Region was designated a Great Lakes Area of Concern in 1987 due to significant degradation of environmental quality and impaired beneficial uses, including Degradation of Aesthetics. Historically overflows of combined sewers, direct discharge of poorly treated industrial wastewater, contaminated storm water, and litter contributed to excessive floating debris, odour and unnatural turbidity along parts of the waterfront and in some sections of Area of Concern watersheds. Despite the perception of poor aesthetic quality of these waters, little to no monitoring was carried out to assess the Degradation of Aesthetics Beneficial Use Impairment due to the challenge of reporting aesthetics in a quantifiable, unbiased manner. Here we describe the qualitative monitoring program implemented in the Toronto region to assess the aesthetic condition of local watersheds. An Aesthetic Quality Index developed for use by Areas of Concern was adapted by taking advantage of existing monitoring programs and local expertise. Results of the assessment indicate that no persistent objectionable deposit, unnatural colour or turbidity, or unnatural odour was present in the Toronto region during the period of study.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".