EQUIPMENT RESOURCE INVENTORY FOR OIL SPILL RESPONSES
Bibliographic record
Abstract
ABSTRACT This paper will describe the online cataloging of oil spill equipment that has taken place on the West Coast of the United States. A collaborative effort, the cataloging project was developed as a Northwest ad hoc undertaking to meet the equipment-listing requirement of the Area Contingency Plan. The intent was to assemble Oil Spill Response Organizations (OSROs) equipment lists into an Excel® spreadsheet format. Project participants in Washington and Oregon began equipment listing and over time, the process expanded to new members in California and Canada. Individual owners of equipment keep the data up-to-date. All equipment-location moves and acquisition changes are posted to the Internet site, yielding a current resource inventory that can be easily accessed 2417. The computer allows this equipment to be displayed, sorted by type, location, and tracked by date/time. The Excel® spreadsheet data can easily be manipulated to accurately tabulate, among other things, how much boom is available or in use, how much oil can be recovered, and how much oil storage is available. Hard copy equipment lists, which soon became outdated, are a thing of the past. The spreadsheets are used on a weekly basis for drill and spill applications as a tool to assist the Incident Command System's (ICS) Operation, Planning and Logistic sections to assemble, track and order specialized response equipment. The states of Washington and Oregon are using the list as a “database of record.” This is a great tool for the ICS Situation Unit when filling out the Incident Status Summary (ICS Form 209). In addition, individual lines of equipment or equipment systems can easily be printed onto ICS T-cards from Excel® by using a mail-merge program. A uniform Excel®-formatted response-equipment list is flexible, simple to use and easy to access. Undoubtedly, it has contributed to improving response management in the Pacific Northwest.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".