A linear regression model for marine propeller optimization, prototyping and design
Bibliographic record
Abstract
A multiple-variable linear regression direct solution model and a statistical model were developed for marine propeller design, optimization and prototype. Computing implementation for the direct solution model was made to create an integrated tool for the marine propeller development process. An error analysis for a simple case with only 4 independent variables was performed. This direct solution model was constructed to provide two functionalities: generation of a set of linear regression coefficients to establish a multiple-variable polynomial equation and interpolation of the multiple-variable data set that are generated by the polynomial equations. An application case was given using a set of data from a marine nozzle propeller series both to cover interpolation to produce curves and linear regression coefficients for interpolation, for both the direct solution model and the statistical model that was computed under a commercial software package. Though much higher than the statistical model, interpolation by the direct solution model showed an error of less than one-tenth of a percent for a group of nozzle propellers. The highly computing-efficient direct solution method showed its capability as a general-purpose linear regression tool which can be applied widely for optimal product prototyping and design.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".