LAS: A programming language and development environment for learning matrix structural analysis
Bibliographic record
Abstract
ABSTRACT This article presents language for the analysis of structures (LAS). It is a development environment and a high‐level language for learning matrix structural analysis, dynamics of structures, and the finite element method. LAS is a flexible learning environment in which users list pre‐programmed commands in the right order to solve finite element static and dynamic problems. In addition to structural analysis commands, the language also includes powerful operators, conditional expressions, loop expressions, and several functions (matrix manipulations, linear algebra, direct stiffness assembly, modal analysis, time domain dynamic analysis, frequency domain dynamic analysis). The LAS environment includes an editor used to list the commands, a matrix manager, a graphics post‐processor, and a Fourier‐analysis tool. © 2015 Wiley Periodicals, Inc. Comput. Appl. Eng. Educ. 24:89–100, 2016; View this article online at wileyonlinelibrary.com/journal/cae ; DOI 10.1002/cae.21675
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.049 | 0.025 |
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".