Urban Oil Spills as a Non-Point Pollution Source in the Golden Horseshoe of Southern Ontario
Bibliographic record
Abstract
Abstract Being the economic engine of Canada and the home of 5 million people, the environmental health of the Golden Horseshoe is very important. Among various pollution sources into the lake, urban oil spills as a non-point pollution source have not caught the attention of most residents. These spills can cause terrestrial impacts by poisoning animals and plants, groundwater contamination by infiltration, and surface water pollution by algal bloom and fish kills and destruction of freshwater invertebrates and vertebrates. In order to investigate the significance of this pollution source, 10 years of spill records in the Golden Horseshoe have been compiled. On the average, about 1050 L per day of oil escaped to the land, water and air environment in this region. About one-third of these spills eventually entered Lake Ontario. Among various types of spilled oil, gasoline, diesel fuel, aviation fuel and furnace oil accounted for the highest reported volume. The former Metropolitan Toronto led the frequency and volume of spills, while Hamilton-Wentworth followed closely. Spills frequently occur on roads, at service stations and at electrical transformers, while the highest spill event volumes occur at bulk plants/terminals/depots and at refineries. The predominant causes of spills are related to leaks from containers, pipes and hoses, and cooling systems. However, the principal reasons for oil spills are human error and equipment failure. The transportation, public and petroleum sectors are responsible for 60% of the reported spill cases, while the petroleum sector alone accounts for nearly 50% of the reported spill volume. Given the significant volume of spilled oil, it is important that all levels of government and private industries increase their effort to promote pollution prevention such as preventive maintenance, improved employee training and/or retraining, and proper vigilant supervision. Additionally, control devices such as oil-water interceptors should be sized properly and implemented at strategic location across the Golden Horseshoe.
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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 | 0.007 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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; both teacher heads agree on what is shown here.
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".