Bibliographic record
Abstract
The City of Toronto has experienced about 300 oil spills per year (Li 1997). Traditionally, the city recommends that businesses and industries practise pollution prevention and install on-site oil separators. Currently, the sizing criteria for these devices are not well defined and the draft code of practices for oil separators by the Canadian Petroleum Product Institute (1994) has not yet been approved by the Ministry of the Environment (Li 2000). Thus, the city is currently investigating the possibility of installing oil separators at spill prone sewer outfalls. The new application of oil separators at sewer outfalls requires that the devices be operable under high flow conditions and that their capacity should reflect the land use characteristics in the associated sewershed. This study has developed an innovative spill control device for the Humber Creek outfall and a Geographic Information System (GIS)-based analysis technique for urban oil spill management. First, a flow diversion structure was designed to capture the dry weather flow at the outfan and to transport the captured flow into an oil/water separator designed in accordance to the American Petroleum Institute's manual (1990). The designs of the flow diversion structure and the oil/water separator were evaluated by a physical model study using the National Water Research Institute's Hydraulics Laboratory at the Canada Centre for Inland Waters in Burlington, Ontario. Then, the GIS-based analysis technique was used to identify potential treatment options for spill-prone sewer outfall in the Town of Richmond Hill. It was found that (I) the spill event characteristics should be analyzed in order to develop design criteria for oil spill control systems; (2) the preliminary design of the oil spill control system at Humber Creek was different from the API's methodology; and (3) the physical model investigation confirmed the conveyance capacity of the diversion channel and the general behaviour of the tilted-plate separator. A database of oil spill records in the Greater Toronto Area from 1988 to 2000 were compiled and geo-referenced. By overlaying the spill characteristics and other GIS data layers, such as woodlots, wetlands and watercourses, spill prone areas were identified. In order to increase the accuracy of the analysis, the percentage of georeference oil spill locations should be increased.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.007 | 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".