Scientific Computing and Visualization with Maple in Economics and Economic Research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it