Bibliographic record
Abstract
ABSTRACT The States/British Columbia Oil Spill Task Force, whose members are the oil spill prevention and response agencies in the U.S. West Coast states of Alaska, Washington, Oregon, and California, as well as the Canadian province of British Columbia initiated a study of West Coast pilotage in 1995. Following a review of national pilotage studies done in both the United States and Canada, a workgroup of representatives from West Coast pilot organizations assisted the Oil Spill Task Force in drafting a survey that was sent to 28 pilotage organizations, governing boards, and authorities on the West Coast. The survey included questions grouped under the following headings: Organizational Description, Organizational Policies and Programs, Organizational Accountability, Pilot Licensing and Qualifications, Pilot Training and Continuing Education, Pilot/Ship Interactions, and an “Other” category. In the second phase of the project, a more diverse set of stakeholders assisted the task force in reviewing the survey responses, discussing the issues raised, and drafting a report with recommendations. Although targeted at West Coast pilots, who were the focus of the study, these recommendations are appropriate for consideration by pilots operating anywhere in the world.
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 | 0.011 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".