IMPROVING THE SHORELINE ASSESSMENT PROCESS WITH NEW SCAT FORMS
Bibliographic record
Abstract
ABSTRACT The shoreline assessment process is an integral component of oil spill response, providing assistance in decision-making and documentation for shoreline cleanup. The process consists of developing cleanup recommendations and target cleanup endpoints, providing standard methods for conducting field surveys and collecting data, designing reporting activities, and setting procedures for shoreline inspection and post-treatment sign-off. Both the National Oceanic and Atmospheric Administration (NOAA) and Environment Canada have recently revised their guidance manuals and forms, making them more effective and consistent. New, third-generation, forms have been generated, including: (1) a standard shoreline assessment form, (2) a “short” form, (3) a tarball form, (4) a wetlands form, and (5) a revised sketch map base. Environment Canada has also generated a tidal flat form and variations of the basic forms for lakes, rivers, streams, arctic coasts, snow and ice, coral reefs, and mangroves. The changes have been made to remedy problems encountered with previous forms, particularly frequent failures by teams to properly record all of the required information; perceptions that the forms were too complex; the need for a good “short” form that meets immediate, operational demands in the face of extremely short time frames; and use of different forms by different groups.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.042 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.045 | 0.023 |
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 source (direct Gemma or distilled Codex), 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".