Monitoring biodiversity on highly reactive rock-paper-scissors models
Bibliographic record
Abstract
This work investigates how biodiversity is affected in a cyclic spatial May-Leonard model with hierarchical and non-hierarchical rules. Here we propose a generalization of the traditional rock-paper-scissors model by considering highly reactive species, i.e., species that react in a stronger manner compared to the others in respect to either competition or reproduction. These two classes of models, called here Highly Competitive and Highly Reproductive models, may lead to hierarchical and non-hierarchical dynamics, depending on the number of highly reactive species. The fundamental feature of these models is the fact that hierarchical models may as well support biodiversity, however, with a higher probability of extinction than the non-hierarchical ones, which are in fact more robust. This analysis is done by evaluating the probability of extinction as a function of mobility. In particular, we have analyzed how the dominance scheme changes depending on the highly reactive species for non-hierarchical models, where the findings lead to the conclusion that highly reactive species are usually at a disadvantage compared to the others. Moreover, we have investigated the power spectrum and the characteristic length of each species, including more information on the behavior of the several systems considered in the present work.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".