Bibliographic record
Abstract
ABSTRACT Historically, many response exercises conducted by the United States Coast Guard and other oil spill response stakeholders have been conducted as functional or full-scale exercises. With the increased demands placed on many U.S. agencies as a result of the terrorist attacks of September 11’ 2001, there is a greater need than ever to ensure that time spent in training and exercises produces positive and tangible results for the participants. In preparation for the joint US/Canadian response exercise, CANUSLANT 2002, the U.S. and Canadian Coast Guards decided to take a step back and look at the lessons learned from previous exercises. Based on this review, the Joint Response Team (JRT) decided to focus CANUSLANT 2002 as a training opportunity and to work on the lessons learned that were repeatedly identified in earlier CANUSLANT exercises. Perhaps the most common exercise conducted in oil spill response is the functional “command post” exercise where exercise participants are assigned to ICS (Incident Command System) staff elements. Participants then respond to an exercise scenario and prescripted injects that are provided to drive participant actions. With personnel turnover, transfers, and increased operational demands, many exercise participants struggle through the crisis phase of an incident scenario and never have the opportunity to learn what it is they are supposed to be doing. When all is said and done, many exercise participants are often simply go home happy that the exercise is over and done with. The goal for CANUSLANT 2002 was to produce an exercise where the participants accomplished something tangible; that long pending issues would be discussed and perhaps even resolved. The Exercise Design Team hoped that the participants walked away from the exercise saying that it was time well spent and not simply thankful that the exercise was over. This paper outlines the factors that led to the success of the CANUSLANT 2002 cross border response exercise. This paper also highlights some of the fundamentals for varying your approach to exercises to achieve tangible results while providing personnel the skills and training required to respond in the event of a real disaster.
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 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.031 | 0.015 |
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".