An island biogeography model for beta diversity and endemism: The roles of speciation, extinction and dispersal
Why this work is in the frame
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Bibliographic record
Abstract
A community composition island biogeography model was developed to explain and predict two community patterns (beta diversity and endemism) with the consideration of speciation, extinction and dispersal processes. Results showed that rate of speciation is positively and linearly associated with beta diversity and endemism, that is, increasing species rates typically could increase the percentage of both endemism and beta diversity. The influences of immigration and extinction rates on beta diversity and endemism are nonlinear, but with numerical simulation, I could observe that increasing extinction rates would lead to decreasing percentage of endemism and beta diversity. The role of immigration rate is very similar to that of speciation rate, having a positive relationship with beta diversity and endemism. Finally, I found that beta diversity is closely related to the percentage of endemism. The slope of this positive relationship is determined jointly by different combinations of speciation, extinction and immigration rates.
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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