Investigating The Effects of Food Chain on Sympatric Speciation Using ECOSIM
Bibliographic record
Abstract
The study of sympatric speciation in evolutionary biology is facing the obstacle of unifying empirical studies with existing theoretical investigations. Disruptive selection due to preferential food resource usage is considered as the main hypothesis to explain the sympatric speciation occurrence in empirical studies. We extend an individual based evolving predator-prey ecosystem platform called "EcoSim" [Gras et al. 2009a] to model a dual resource system. We investigated whether and in which conditions the selective pressures acting on foraging behaviors drove sympatric speciation. We observed clear results showing some behavioral modifications occurring as a consequence of preferential resource usage. We also observed many cases where the sympatric speciation criteria described in the literature were fulfilled. Using several machine learning techniques, we extracted explicit rules that can predict with a very high accuracy the occurrence of sympatric speciation based on ecological factor observations. Moreover, we confirmed that the existence of a second food resource is determinant for the emergence of sympatric phenomenon. We also proved that our method is able to discover very generic rules which may later be used to structure empirical studies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".