OBJECT-ORIENTED APPROACH FOR THE ASSESSMENT OF MOMENTCURVATURE RELATIONSHIP OF A VARYING-WIDHT AND MULTI-MATERIAL BEAM CROSS SECTION
Bibliographic record
Abstract
This research developed the rational approach to shear design in 1984 Canadian Code Provision into a new approach which is object-oriented in fashion, and presented it for the purpose of assessing moment-curvature-relationship of a varying-width and multimaterial beam cross section. Unlike the traditional method that views a cross section as a single entity, this new approach views a section as a composition of autonomous objects. In this approach, a cross section is recognized as a system which is made up of objects, of which each can be predicated uniquely; behave autonomously in responding to loading, and capable of communication between each other. Being in such a fashion, the approach was shown to be capable to faithfully represent a section which varies in width, and is made up of materials with different mechanical characteristics, in whatever possible arrangement. To compensate for the painstaking computation that may be involved in the approach, and maintain its object-oriented fashion, an-objectoriented computer-software that uses an object-oriented user interface platform was recommended to be provided as an auxiliary to the approach.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".