SELECTION AND USE OF SHORELINE TREATMENT ENDPOINTS FOR OIL SPILL RESPONSE
Bibliographic record
Abstract
ABSTRACT Shoreline treatment or shoreline cleanup endpoints are specific criteria assigned to a segment or unit of oiled shoreline or river bank that are used to define when sufficient treatment effort has been completed for that segment or unit. In effect, the endpoints are the practical definition of ‘clean for that particular segment of shoreline in that particular spill. The selection of appropriate and practical end points is part of the net environmental benefit evaluation in the decision process that is conducted during the development of the shoreline treatment plan. Endpoints affect the selection of response strategies and tactics, provide a target for the operations team, and are a standard against which the achievement of treatment can be compared so that closure can be achieved. This paper addresses endpoints in the context of the oiled-shoreline treatment decision process. The concepts and principles involved in the selection of endpoint criteria and measurement techniques are described. Explanations and examples are provided that can be used as a framework to guide and structure this vital element of the decision-making process. Three fundamentally different approaches to define and measure endpoints are identified; these being based on (a) analytical measurements, (b) judgements of impact assessment or (c) visual field measurements of the quantity and nature of oil. A step-wise guide is presented that can be used as a tool to assist in the selection of descriptors and phasing for endpoints based on qualitative/quantitative field observations using SCAT (Shoreline Cleanup Assessment Team) terminology.
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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 | 0.021 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".