Bibliographic record
Abstract
Recent research efforts have focused on the analysis of environmental systems using cellular models. Although most of the existing solutions are based on the cellular automata formalism, this technique has some problems that constrain its power, usability and feasibility for studying large complex systems. Instead, combining cellular automata with discrete event systems specifications (DEVS) showed excellent results in terms of quality and performance. Despite these encouraging results, the environmental science/engineering community still prefers more traditional approaches, as DEVS-based techniques require a fundamental change of the modeling and simulation paradigm, while entailing expertise in advanced programming, distributed computing, etc. Cell-DEVS and the CD++ toolkit were created to address these problems: they simplify the construction of complex cellular models by allowing simple and intuitive model specification. The discrete event nature of the formalism provides better precision and performance, and models can run in different simulation environments (single user, real-time, distributed/parallel) without special expertise required. Environmental applications can be easily constructed, making it possible for users with basic training in the techniques and software tools to face the study of complex problems. We present the definition of different models of environmental applications, including the pollution on a basin, fire spreading, watershed formation and viability of a population, focusing on how to define such applications using Cell-DEVS methodology, using an approach that facilitates this paradigm shift.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".