Bibliographic record
Abstract
Marine performance evaluation was carried out aboard a fast rescue craft utilized by the Canadian Coast Guard. The experiments were conducted in late 2016 in the waters off Conception Bay South, NL and St. John’s, NL. The three primary focus areas of the study were vessel performance, fuel economy, and human kinetics. The evaluated vessel has a unique propulsion arrangement and is the first to be outfitted in Canada with Mercury Marine DSI 3.0 spark-ignited diesel outboard motors. Motivations to use this type of engine are to unify the Coast Guard’s fuel supply and to also allow engine re-start after inversion. The Canadian Coast Guard is interested in this vessel’s performance in comparison to the rest of the fleet because of these intrinsic advantages. The performance tests concluded that the vessel is very reactive to helm input. It also has much greater directional stability as its speed increases. It can reach its maximum speed in 250 metres, taking approximately 20 seconds to do so. At a full speed of 38 knots, the vessel can execute a 180ᴼ turn in just over 200 metres, and just under 20 metres at manoeuvring speed. The vessel can also tow a 19.7 metre fishing vessel at speeds up to 4.5 knots. The trials showed that the fuel economy was not overly sensitive to wind speed, wind direction, or even wave height. The fuel consumption curve fits a resistance curve that is typical of a planing craft. Its maximum range of 56 nautical miles is achieved at its optimal cruising speed of 24.6 knots. The vessel motions show that the accelerations in the Z direction are the most prominent. The accelerations in the X direction are the lowest, with accelerations in the Y directions being slightly higher. The maximum observed Z acceleration was 4.76 times gravity. The helmsman’s ability to maintain heading is increased with speed due to the higher directional stability observed at higher speeds. Wave height also has a prominent effect on the helmsman’s ability to maintain heading.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".