Bibliographic record
Abstract
This paper presents a method for selecting materials, cross-section shapes, and combinations thereof. The novelty of the method is the definition of Shape Transformers. These parameters are dimensionless measures of the geometric quantities of a cross-section. They describe the shape properties of a cross-section regardless of size and are thus invariant to any scaling imposed on the cross-section size. Shape Transformers are valuable with modelling the equation of mechanics and with the development of selection charts for optimum design. The rationale of the approach is that the fundamental equations of mechanics can be expressed by a product of four separable factors: the functional requirements, the material properties, the Shape Transformers, and the geometric quantities of a rectangle as defined by its cross-section size. This permits general expressions of performance indices to be derived for any scaling transformation. For example, indices for selecting materials and cross-sectional shapes that minimize the mass of beams are given for stiffness design. The last part of the paper illustrates how Shape Transformers facilitate a graphical exploration of performance data. The whole range of cross-sectional shapes can be visualized at a glance for each material. Lines of iso-performance enable efficiency comparison of materials and/or shapes for a given cross-section scaling. Shape Transformers assist design choices and give insight into optimum selection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.009 |
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".