Phase-II experimental uncertainty analysis for ice tank ship resistance experiments using a model for a Canadian icebreaker "Terry Fox"
Bibliographic record
Abstract
This is the 2nd phase of a research program designed to develop a procedure for Experimental Uncertainty Analysis (EUA) for ice tank ship testing. The latter is a task for the 23rd and 24th ITTC specialist committee on ice. In this report, the results of Phase-II ship resistance in ice test program (IMD report TR-2003-07) were used to formulate the EUA procedure. The "total uncertainty" is the sum of a "bias uncertainty" and a "random uncertainty". Bias uncertainties are due to system set up and calibrations of equipments and instrumentation. Random uncertainties are associated with the degree of repeatability (accuracy) of the test results. Major sources for random uncertainties are model misalignments, effects of the surrounding environment (such as change in temperature during testing), human factors, etc. The procedure for EUA presented in this report validates the applicability of the preliminary EUA procedure (proposed by Derradji-Aouat, 2002) to the laboratory measurements from both phases of testing. Consequently, this EUA procedure will be recommended to the 24th ITTC (during its general meeting, Scotland, 2005).
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".