A System Dynamics Modeling Approach for Estimation of Oily Waste Generation From Marine Oil Spill Response: A Case Study of an Oil Spill in Central Coast of British Columbia
Bibliographic record
Abstract
The understanding of waste generation is of critical importance for effective oily waste management in marine oil spill response operation. A system dynamics model was developed in this study to estimate the quantity of oily waste generated from marine oil spill response operations. Various aspects were considered, including weather conditions, spilled oil volume and characteristics, response time, and response methods. The types of oily waste include recovered oil, oily water, oily sorbents, oily personal protection equipment, and oily debris. The model was validated using data collected from an actual oil spill incident in British Columbia, Canada. The comparison of model estimation and observed results showed an average prediction accuracy of 86%. Sensitivity analysis was conducted to examine the impacts of two modeling parameters, including response arrival time and sorbent booms amount. Results of a case study indicated that initiation of response operations 5-h earlier could increased oil recovery by 26%. Furthermore, sensitivity analysis highlighted a 45% overuse of sorbents which resulted in the generation of unnecessary oily solid waste. Response surface methodology (RSM) analysis was applied to analyze the interaction effect of model parameters on model outputs. Results showed a significant interaction between sea temperature and response arrival time on recovered oil and between sorbent boom weight and sorbent booms usage rate on solid waste. The developed model can provide an effective tool for informed waste management decision-making related to marine oil spill response operations.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".