New Trends in Modeling and Simulation in Economic Sciences
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 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).
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 | 0.000 | 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 | 0.000 | 0.000 |
| 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