Estimating pipeline oil spill volumes using environmental site assessment information
Bibliographic record
Abstract
When a reportable oil spill occurs, stakeholders expect the responsible party to issue a release volume estimate as part of the mitigation accountability. One method of estimating a spill volume is to use environmental assessment data to enumerate the total volume of petroleum hydrocarbons detected in the environment. This method was applied to two terrestrial based pipeline spill case studies. The assessment data was used to create a conceptual site model which, along with the liquid petroleum hydrocarbon density and soil bulk density, was synthesized to estimate the spill volumes. The case study data was collected at the time of each release for assessment, delineation, and remediation purposes. Using the data to estimate spill volumes was ancillary and complementary to its original purpose. The practical implication is that environmental assessment and remediation practitioners can readily adopt and implement this method at terrestrial based spills. Keywords: oil spill, mass balance, environmental remediation, pipeline release, spill volume estimate.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 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; both teacher heads agree on what is shown here.
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".