Bibliographic record
Abstract
Oil spills in industrialized cities pose a significant threat to their urban water environment. The largest city in Canada, the city of Toronto, has an average 300-500 oil spills per year with an average total volume of about 160,000 L/year. About 45% of the spills was eventually cleaned up. Given the enormous amount of remaining oil entering into the fragile urban ecosystem, it is important to develop an effective pollution prevention and control plan for the city. A Geographic Information System (GIS) planning model has been developed to characterize oil spills and determine preventive and control measures available in the city. A database of oil spill records from 1988 to 1997 was compiled and geo-referenced. Attributes to each record such as spill volume, oil type, location, road type, sector, source, cleanup percentage, and environmental impacts were created. GIS layers of woodlots, wetlands, watercourses, Environmental Sensitive Areas, and Areas of Natural and Scientific Interest were obtained from the local Conservation Authority. By overlaying the spill characteristics with the GIS layers, evaluation of preventive and control solutions close to these environmental features was conducted. It was found that employee training and preventive maintenance should be improved as the principal cause of spills was attributed to human errors and equipment failure. Additionally, the cost of using oil separators at strategic spill locations was found to be $1.4 million. The GIS model provides an efficient planning tool for urban oil spill management. Additionally, the graphical capability of GIS allows users to integrate environmental features and spill characteristics in the management analysis.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".