<i>Rich Passage 1</i> Wake Validation and Shore Response Study
Bibliographic record
Abstract
Surface waves generated by high-speed ferries operating at trans-critical and super-critical speeds can potentially cause adverse impacts to shorelines in confined waterways and environmentally sensitive areas. Repeated attempts to establish fast passenger ferry service on the Seattle-Bremerton route in Puget Sound have met with limited success due to such impacts along the narrowest portion of the route. This paper presents results from a multi-disciplinary study designed to evaluate the feasibility of re-introducing high-speed, passenger-only ferry service on the Seattle- Bremerton route. Performance of a new low-wake design, foil-assisted catamaran is being tested against wake and impact criteria that were developed from a synthesis of an extensive data set including in-situ physical and biological impact studies, full-scale field trials, and computational modelling studies of candidate vessels. The work is being conducted in two phases: (1) evaluation of the high-speed foil-assisted catamaran (Rich Passage 1) for commercial application in a wake-sensitive area and (2) shore response studies to provide data to understand wake behavior from Rich Passage I near the shoreline and to quantify beach response to wakes. Results indicate Rich Passage 1 can operate at 36 to 37 knots and meet the wake criterion established for the Rich Passage portion of the Seattle-to-Bremerton route during earlier phases of research. Preliminary analysis of data from shore response studies shows substantial variability in response from one shoreline to the next. This variability is primarily attributed to differences in wake power measured at the shoreline resulting from differences in bathymetry, tidal currents, and distance from the vessel sailing line.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".