DP in ice environments—development of a dynamic positioning in ice validation platform (DPIVP)
Bibliographic record
Abstract
This paper presents the development of a dynamic positioning in ice validation platform (DPIVP) which is part of a larger research project aimed at developing dynamic positioning (DP) system technologies for ice-rich environments. One outcome is simulation software to aid research in this area. The DPIVP software was designed to realistically simulate the dynamics of ice-structure interactions for real-time applications and to validate components common to DP in ice simulations. The software consists of many components which the DPIVP ties together as a unified system. All components have well-defined interfaces. Many of them are also distributed, allowing execution on separate computers and/or CPUs which helps ensure real-time operation. These two characteristics also decreases coupling and encourages a more modular design with the benefit of easily substituting alternative component implementations without reprogramming the DPIVP. Alternate implementations are useful for conducting research in specific DP in ice areas without substantially changing the system, such as alternative ice force models, DP control algorithms, vessel models, 3D and 2D visualization, environment models, and data acquisition systems. The integrated system was tested and evaluated using unit testing, integration testing, and system testing. The completed system was also validated using test cases that match physical model tests; the results compared favorably. Although the software has some limitations, for example, validated ice-force models being limited to two vessels, and thus lacks the generality we wish, the end result is a working prototype that satisfies the research requirements and provides an architecture and framework for future development.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".