Bibliographic record
Abstract
Urban oil spills occur frequently at industrial sites and along transportation corridors in North America. Although the cumulative spilled volume is large, there is limited research on how to manage these spills effectively. Under the Great Lakes Water Quality Agreement, Canada and United States have compiled urban spills and reported the trend since 1989. For instance, the Province of Ontario established a Spill Action Centre to collect and report spills in Ontario, and assist municipalities in spill responses. Since 1989, a database of more than 50,000 records has been compiled but no research was performed. Ryerson University research team has started urban spill research since 1998 and developed statistical and probabilistic models to facilitate urban spill management. The first stage of research focused on analyzing the characteristics of urban spills such as spill type, locations, volume, causes and reasons, impact media, and cleanup percentages. These characteristics were used to develop municipal oil spill prevention, control, and response plans. The current stage of research has developed spatial and temporal stochastic spill occurrence models which can be used to determine risk of future spills and management options. This presentation overviews the research applications in urban oil spill management.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".