Niche Theory as an Underutilized Resource for the Study of Adaptive Radiations
Bibliographic record
Abstract
Biologists are often stuck between two opposing questions: Why are there so many species and why are there not more? Although these questions apply to the maintenance of existing species, they equally apply to the formation of new ones. The more species specialize in terms of their niches, the more opportunities arise for new species to form and coexist in communities. What sets an upper limit to specialization, thus setting an upper limit to speciation? We propose that MacArthur's theories of species packing and resource minimization may hold answers. Specifically, resources and individuals are finite-as species become increasingly specialized, each individual has fewer resources it can access. Species can only be as specialized as is possible in a given resource environment while still meeting basic resource requirements. We propose that the upper limit to specialization lies below the threshold that causes populations to be so small that stochastic extinctions take over, and that this limit is likely rarely approached due to the sequential timing by which new lineages arrive.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".