New Trends in Modeling and Simulation in Economic Sciences
Bibliographic record
Abstract
The aim of this paper is to map selected tools offered the Maple system and user supports provided by the Canadian company Maplesoft Inc. for professional and modern implementation in economics, resp. finances. These are mainly fields of scientific computing, in the quantitative modeling, graphics visualizations and interactive simulations, both the direct using of built-in elements and the communication platform supported by Maplesoft. Maple is an efficient tool for the solving problems of various complexities in these areas. It uses the efficient algorithms and methods of mathematical disciplines and executes the numerical and also symbolic calculations. Interactive tools of Maple as Clickable Math, on-line and interactive statistical or numeric computations and visualizations and inspiration of worksheets and documents from the Application Maplesoft Centre are important for the application of quantitative methods in economics and finance. Selected methods are adjusted to aims of article, i.e. to present the financial package and means in the example of mortgage loan. We used the last two current versions of Maple (Releases 16 and 17).
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".