A Review of Numerical Models for Oil Penetration, Retention, and Attenuation on Shorelines
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Oil spills that reach shorelines greatly increase risks to coastal resources. Understanding how long oil is likely to remain on a shoreline is important in deciding response priorities, areas to clean, and the degree of intervention recommended. Wave action, tides, and currents can relocate oil laterally along the beach, cause oil to penetrate vertically into the sediments, and remove oil from the shoreline. Physico-chemical processes transfer some hydrocarbons to the atmosphere and to the adjacent water column resulting in diminished oil on the shoreline. Oil dispersion, through formation of oil-particulate aggregates, and microbial degradation processes can break down a large fraction of the residual oil remaining on and within shorelines. A comprehensive review of the scientific literature reveals that although there are many models that describe and predict oil transport, behavior, and fate in the sea, few numerical models have been developed for oil stranded on shorelines. Canada’s Multi-Partner Research Initiative Program aims to develop a model-based “Decision Support Tool†that can predict the rates of oil loss that can be achieved from natural attenuation processes and the application of active spill response strategies. This model is built on the understanding of factors controlling: penetration, holding capacity, retention, and the residual capacity (persistence) of oil stranded on shorelines derived from the results of case histories, laboratory, meso-scale tests and field trials. Output from the model is intended to support spill response decision-making by allowing spill responders and the public to visualize the results achieved by natural attenuation versus remedial strategies.
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.
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) | 0.001 | 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) | 0.000 | 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 it