Speciation and patterns of biodiversity
Bibliographic record
Abstract
There are many more species of insects (>850,000) than of their putative sister taxon (Entognatha, 7500 species) (Mayhew 2002). More than 1600 species of birds have been recorded near the Equator in the New World compared with 300–400 species at latitudes around 40° North or South (Gaston & Blackburn 2000). Mammalian families with average body sizes around 10 g have nearly 10 times as many species as those with average body sizes around 3 kg (Purvis et al . 2003). In a catch of 15,609 moths of 240 species over 4 years of light trapping at Rothamsted, England, the majority of species (180) were represented by 50 individuals or less (Fisher et al . 1943). These observations illustrate the highly uneven distribution of the world's biological diversity. They are examples of four well-known patterns: species richness varies among clades; it varies spatially, with the latitudinal gradient being a classic example; it is higher in small animals than large ones; and rare species are more numerous than common ones. Documenting and explaining such patterns is a major enterprise of ecology (Gaston & Blackburn 2000). In their introduction to a previous British Ecological Society (BES) Symposium Volume, Blackburn and Gaston (2003) identified three evolutionary processes that underlie large-scale patterns of biodiversity: speciation, extinction and range changes. Anagenetic change might also contribute to some patterns, for example if there is a general tendency for size increase among mammalian lineages (Alroy 1998).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".