Bibliographic record
Abstract
The discipline of modeling and simulation (M&S) is advancing, maturing, and is being used in more and more challenging areas. Appreciation of its comprehensive and integrative view, i.e., its big picture would be very useful for its continued and systematic growth and successful applications. The article starts with a rationale for the needs to see the big picture of M&S and ways to see the big picture. Since M&S is closely related with reality and its representations i.e., models, reality/model dichotomy is clarified. As the core of the article, detailed perceptions of M&S from different perspectives such as: purpose of use, problem to be solved, connectivity of operations, and types of knowledge processing are clarified. Then, three aspects of ways to increase the trustworthiness of M&S are outlined (i.e., validation and verification (V&V), quality assurance (QA), and failure avoidance (FA). The article ends with a discussion of M&S from the perspective of professionalism. Some recommendations or challenges are still open for implementations for the success of M&S.
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.
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 | 0.016 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.019 | 0.038 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".