ESTIMATING MECHANICAL OIL RECOVERY WITH THE RESPONSE OPTIONS CALCULATOR
Why this work is in the frame
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Bibliographic record
Abstract
ABSTRACT Forecasting the actual effectiveness of mechanical oil spill response forces is known to be very difficult. Frequently, linear calculations such as estimated daily recovery capacity (EDRC) are used to predict the volume of oil that a response system can recover. While EDRC provides a standard approach to estimating on-water oil recovery based on a percentage of the skimming efficiency, this approach does not account for all of the real-world factors that may impact the actual recovery capacity of a given response force. We have developed a method using the Response Options Calculator (ROC) program to estimate the on-water recovery capacity for a defined response force under various oil spill scenarios, incorporating transit times, spill timing, seasonality, and simplified environmental conditions. This provides more realistic recovery estimates than EDRC, and can be developed using a publicly available modeling tool that does not require a technical background. This paper describes our recent experience applying the ROC to a series of hypothetical oil spills along the Pacific Coast of the U.S. and Canada. We explore the capabilities and limitations ROC, and explain the method we have developed. Our treatment includes a discussion of factors such as secondary storage, transit times, spill timing, seasonality and daylight, as well as model shortcomings and how to interpret the final outputs. The results produced by the ROC analysis may be used to inform oil spill contingency planning, response readiness assessments, and risk management.
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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 | 0.000 |
| 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) | 0.001 | 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 it