Individual-Based Modeling and Nonlinear Analysis for Complex Systems with Application to Theoretical Ecology
Bibliographic record
Abstract
One approach to understanding the behaviour of complex systems is individual-based modeling, which provides a bottom-up approach allowing for the consideration of the traits and behaviour of individual organisms. Ecosystem models aim to characterize the major dynamics of ecosystems, in order to synthesize the understanding of such systems and to allow predictions of their behaviour. Moreover, ecosystem simulations have the potential to help scientists address theoretical questions as well as helping with ecological resource management. Because in reality biologists do not have much data regarding variations in ecosystems over long periods of time, using the results of ecological computer simulation for making reasonable predictions can help biologists to better understand the long-term behaviour of ecosystems. Different versions of ecosystem simulations have been developed to investigate several questions in ecology such as how speciation proceeds in the absence of experimenter-defined functions. I have investigated some of these questions relying on complex interactions between the many individuals involved in the system, as well as long-term evolutionary patterns and processes such as speciation and macroevolution. Most scientists now believe that natural phenomena have to be looking as a chaotic system. In the past few years, chaos analysis techniques have gained increasing attention over a variety of applications. I have analyzed results of complex models to see whether chaotic behaviour can emerge, since any attempt to model a realistic system needs to have the capacity to generate patterns as complex as the ones that are observed in real systems. To further understand the complex behaviour of real systems, a new algorithm for long-term prediction of time series behaviour is also proposed based on chaos analysis. We evaluated the performance of our new method with respect to the prediction of the Dow-Jones industrial index time series, epileptic seizure and global temperature anomaly.
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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 | 0.003 |
| 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 | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".