Design and implementation of a model scale lifeboat deployment system
Bibliographic record
Abstract
Design of a model scale lifeboat deployment system is presented. The lifeboat deployment system facilitates testing of physical model lifeboats. It is comprised of a controlling application and three core subsystems; a wave timer, two davits and associated controllers, and a boom and associated controller. The controlling application presents an intuitive graphical user interface and provides configuration and operational control of the entire system. The wave timer analyzes data from a wave probe to determine the optimum time a lifeboat should be launched. This process ensures the lifeboat lands on the least dangerous part of the wave phase. The wave timer and automated launch capability enable researchers to study the effects of deploying the lifeboat at differing wave phases. The davits and controller subsystem raises and lowers the lifeboat at user specified controlled rates. The boom and controller subsystem orients and propels the lifeboat away after launch. The angle of the boom, which is dependent on boom load, is continually adjusted by a simple closed loop feedback system. Discussion of the design requirements and implementation of this system are presented.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".