Experimental uncertainty analysis for ship model testing in the ice tank
Bibliographic record
Abstract
Historically, until late 1980's, only marginal work on Experimental Uncertainty Analysis (EUA) was reported by ocean/marine test facilities. During the 1990's, the International Towing Tank Conference (ITTC) and the International Ship and Offshore Structure Congress (ISSC) have recommended and supported the application of Uncertainty Analysis (UA) in both experimental and numerical/computational fields. The work presented in this document deals exclusively with Experimental Uncertainties (EU) in the results obtained from testing of model ships in a typical ice tank testing facility. Up to now, in the literature, there are no standards to quantify and/or minimize uncertainties in ice tank testing. The objective of this work is to develop a method of analysis for EU in typical ice tank ship experiments. In fact, this objective is a task for the 24th ITTC Specialist Committee on Ice (2002-2005). To achieve this objective, experiments for ship resistance in ice were conducted at the Institute for Ocean Technology of the National Research Council of Canada (www.iot-ito.nrc-cnrc.gc.ca/) using a model for the Canadian Icebreaker 'The Terry Fox'. The data obtained from these tests was used to develop a procedure for EUA in ice tank ship resistance tests. From the project management point of view, the ice tank test program was divided into several phases to accommodate the planning for opportunity testing. So far, three phases of testing have been completed. Phases I and II of the test program were already documented (Derradji-Aouat, 2004). In this paper, the results from Phase III are reported. The methodology developed to quantify EU in the test results is presented and validated. Also, comparisons of test results and analyses from the previous tow phases (Phases I and II) of the test program are compared to those from Phase III.
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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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".