UMAR theory & user manual: version 1.0
Bibliographic record
Abstract
Program UMAR, which stands for Unified Method of Analysis for Rehabilitation, is a general-purpose finite element program for rehabilitation analysis and design of various structural and building components. UMAR solves for the static response of linear and non-linear two- and three-dimensional structural systems under the action of mechanical loads. The computer program can include time-dependent phenomena such as creep, shrinkage and cyclic loading. UMAR also offers transient analysis capabilities for parabolic initial-value problems in fluid mechanics, and it can couple this type of analysis with that due to mechanical loads. Some features which are available in the program include:Multi-field/physics analysis capabilities to couple heat-mass transfer problems with stress problems;Element options to model addition or removal of elements (material) in the physical system to simulate construction/rehabilitation processes;Six basic elements for structural analysis: a space truss element, a shear connector element, a quadrilateral membrane element, a quadrilateral plate-bending element, a quadrilateral shell element, and a layered-solid element. All the quadrilateral elements can be layered in the out-of-plane direction. The membrane and solid elements can also be used for transient analysis;Prescribed nodal displacements and/or surface traction options for structural analysis;Prescribed nodal values and/or surface flux options for transient analysis;Prescribed arbitrary load-time functions (both mechanical and environmental);Comprehensive library of material models: isotropic linear-elastic, orthotropic linear-elastic, transversely-isotropic linear-elastic, orthotropic hypo-elastic, elasto-(plastic)-damage, and a family of multi-yield elasto(-visco)-plastic;Several symmetric and non-symmetric matrix equation solvers: LU decomposition solvers for banded and full matrices, a sky-line solver for banded matrices, a domain decomposition solver, a symmetric frontal solver, and a Gauss-Seidel iterative solver;ASCII input file, where data is organised into recognisable blocks. Program UMAR is written in Fortran 90, and the executable is available for PC computing platforms by compiling and linking the code with the Compaq Visual Fortran 6.1a compiler.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.301 | 0.216 |
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".