LOGISTICS FOR THE CONDUCT OF CONTROLLED OIL SPILL EXPERIMENTON BIORESTORATION OF FRESHWATER WETLANDS
Bibliographic record
Abstract
ABSTRACT The St. Lawrence River, situated between Canada and the United States, provides a major transport route in North America for the transport of millions of tons of crude oil, condensates, and refined products each year. In addition, as one of the largest rivers in the world, it is of major ecological significance. For example, over 55,000 hectares of wetlands are found along the St. Lawrence alone. These areas provide habitat for wildlife, the nurseries for fisheries, and control coastal erosion are highly vulnerable to oil spills. Furthermore, as traditional oil spill cleanup methods may be ineffective or cause more damage, emergency responders are considering less intrusive methods such as biorestoration as operational countermeasures. A biorestoration experiment was designed to measure the effectiveness of this method in the St. Lawrence River. To conduct this experiment, 1,200 liters of crude oil were to be spilled in a controlled manner over an experimental zone of 750 m2 in a marsh area. To obtain regulatory approvals from governmental agencies, environmental groups and, more importantly, to avoid the “not in my backyard” protests from the local communities, site selection, emergency planning, contingency measures, and especially community meetings, were all necessary steps towards the acceptance of the project. This controlled spill was done in June 1998 without any incident. Sampling of the experimental site will be completed in the fall of 2000. This paper aims to provide insights on the steps needed to gain acceptance from concerned citizens for the conduct of a controlled oil spill experiment.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".