Scientific Computing and Visualization with Maple in Economics and Economic Research
Bibliographic record
Abstract
The aim of this paper is to map selected tools offered by Maple and user support provided by Maplesoft Inc. for professional and modern implementation in the field of scientific computing, modeling and visualizations in economics. Such support will be a significant technical advantage in time for use in economic research. The paper analyzes the latest version of the mathematical software Maple for scientific computing in economics and finance. It terms of its implementation in the quantitative modeling, calculations and graphics visualizations, both the direct using of built-in elements and the communication platform supported by the Canadian company Maplesoft Inc. that has developed Maple since 1980. Solutions of economic problems are intimately linked in the number of areas of society. At present, continuous innovations and using of new information technologies is trend in science, education and researches that occurs all over the world. Our efforts in this analysis are one of the preparatory stages to meet the primary objective of the solution of the project ""Construction of a complex multi-methods evaluation of performance in selected sectors"" (Reg. No. P403/11/2085) realized at the Brno University of Technology and the Mendel University in Brno.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.030 | 0.014 |
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".