Bibliographic record
Abstract
Planar motion mechanism (PMM) test techniques are used to determine hydrodynamic coefficients for mathematical models of ship manoeuvring [1]. An IOT PMM [2] was commissioned in 1996 and has been used successfully in a number of tank tests since then. The 1996 version of reference [2] was released when the PMM was commissioned. Since then, the apparatus has undergone many changes. The 2005 version describes the current PMM control system. The first part is a user manual and the second, a programmer's guide. The PMM control has six built-in standard tests: static tests (model yawed), sway only, sway and yaw coupled (pure yaw), coupled motion with static yaw, surge, and turning circles (constant yaw rate). In September 2004, a small group began to investigate PMM use and capability at IOT, with the main objective of enhancing the institute's capability in this area. Group members are Paul Thorburn (Chair), Christopher Williams, David Molyneux, Michael Sullivan, Michael Lau, Tony Randell and Don Spencer (Oceanic). Initial objectives were: 1.1 determine requirements for a PMM 1.2 produce a concept design to meet the requirements 1.3 provide a cost estimate for recommendations. Recommendations may include improvements the present PMM and/or a proposal for a new PMM A previous investigation in 2002 ended when there was no budget available to pursue improvements to the IOT device. A series of meetings began on September 28, 2004 and is continuing. Discussions, actions and results to date are outlined in this report. All information related to this project is available on an IOT computer network folder (pccommon\PMM).
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.156 | 0.083 |
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".