Bibliographic record
Abstract
A submerged structure has virtual mass matrix in the inertial term and wet mass matrix in the driving term in the equation of motion, whereas a dry structure has the same mass matrix in these two terms. If a finite element code for dry structures is used to analyze a submerged structure, the results should be adjusted. Result for a Single Degree of Freedom (SDOF) system is scaled by the ratio of wet mass to virtual mass. Results for a Multiple Degree of Freedom (MDOF) system are adjusted by scaling the Floor Response Spectra (FRS) at each modal frequency by a ratio of modal work done in that mode by the wet mass matrix to that by the virtual mass matrix. The adjustment factor can be estimated as a product of a ratio of participation factors and a ratio of modal masses. Both the participation factors and the modal masses are computed by using the wet mass matrix, and then by using the virtual mass matrix, respectively. This method provides an alternative way to assess the margin in the design based on the current FRS method that does not distinguish the difference between the two mass matrices. More studies should be carried out to implement this method in specific practical applications.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.008 |
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".