Applying Cell-DEVS Methodology for Modeling the Environment
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
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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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