Tank Barge ARGO: A Case Study on the Employment of NCP Special Teams
Bibliographic record
Abstract
Abstract On October 20, 1937 the Tank Barge ARGO sank during a fall storm on western Lake Erie. The barge was reported to be carrying 100,000 gallons of crude oil and 100,000 gallons of benzol and was thought to have sunk in Canadian waters. The ARGO was identified as a potential polluting wreck in NOAA’s Remediation of Underwater Legacy Environmental Threat (RULET) project and ranked as the greatest legacy underwater environmental threat on the Great Lakes. However, the exact location, condition, and disposition of the sunken barge and its cargo were a mystery for over 78 years. On August 28, 2015 the Tank Barge ARGO was discovered by the Cleveland Underwater Explorers in U.S. waters, beginning a three month response by the U.S. Coast Guard Federal On-Scene Coordinator to mitigate the substantial threat to the environment and public health posed by the ARGO’s cargo. Rife with challenges, the response included complex dive operations, hot tapping, chemical lightering and storage, environmental protection and monitoring and severe logistical constraints, all of which required an extensive incident management organization and utilized almost every “special team” under the National Contingency Plan. This case study summarizes the response to the Tank Barge ARGO and details how “special teams” were utilized by the Federal On-Scene Coordinator to safely and effectively respond to the environmental threat. Specifically, the capabilities of the National Strike Force, District Response Group and District Response Advisory Team, and Scientific Support Coordinator during this response are highlighted and offered as a best practice for other oil and hazardous substance responses.
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.
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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".