Bibliographic record
Abstract
Oil spills occurring on land have accounted for at least one third or over 24,000 of all land spills (approximately 76,000) of various substances reported in Ontario from 1988 to 2013. The objective of this research was to develop a comprehensive planning framework for urban inland oil spill management encompassing all three stages of spill management: (1) prevention, (2) control and (3) response. An inland oil spill database was developed and the source of each spill was analyzed. Preliminary analyses determined that approximately 46 % of spills occur at fixed locations (stationary spills), 21 % of spills involve moving vehicles (transportation-related), 13 % involve moving vehicle accidents (transportation-related accidents) and 20 % were categorized as other kinds of spills. Sub-databases were developed for both stationary and transportation-related spills which include numeric and non-numeric data variables. Hot spot analyses (optimized version) were performed on a subset of transportation-related spills to develop a highway spill model. The highway spill model illustrates that the majority of highway spills (75 %) occur at interchanges and the remaining spills occur either on the highway (8 %) or at unknown locations (17%). A macro program was developed to simulate future spill events based on historical spill events of gasoline spills within the case study area. The variables under study were fitted with distributions and Monte Carlo or Iman Conover methods were used to generate simulation results spreadsheets of spill series data based on the fitted distributions. The final macro program generated 30,000 simulation results spreadsheets and compiled the results in an aggregate spreadsheet. Descriptive statistics of the numeric variables were calculated and used to recommend spill management strategies. A simulation results spreadsheet with predicted spill records was used to develop a Geographic Information Systems (GIS) model to delineate spill pathways and calculate travel-time for overland flow and channel flow within the storm sewer system (geometric network). The model delineates the overland spill path and traces the spill path through the storm sewer network. The travel-time for each type of path is calculated and can be summed to determine the total travel-time for each predicted spill. Keywords: inland oil spill, comprehensive planning framework, spill management, prevention, control, response, stationary, transportation-related, hot spot analysis, macro program, Monte Carlo, Iman Conover, simulation, GIS, travel-time, spill path, geometric network
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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 | 0.000 | 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 | 0.001 |
| 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 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".