Bibliographic record
Abstract
The ecological niche plays a central role in the process of ‘ecological speciation’, in which divergent selection between niches drives the evolution of reproductive isolation (Muller 1942; Mayr 1947, 1963; Schluter & Nagel 1995; Funk 1998; Schluter 2000). Ecological by-product speciation occurs because ecological traits that have diverged between populations via divergent selection, or traits that are genetically correlated with such traits, incidentally affect reproductive isolation. This process can occur under any geographic arrangement of populations (e.g. allopatry, parapatry or sympatry). A central prediction of ecological speciation is that ecologically divergent pairs of populations will exhibit greater levels of reproductive isolation than ecologically similar pairs of populations of similar age. Another prediction is that traits under divergent selection, or those genetically correlated with them, should often incidentally affect reproductive isolation (e.g. mate preference, hybrid fitness). In recent years, these predictions have been supported in a range of taxa (see Feder et al . 1994; Funk 1998; Via 1999; Rundle et al . 2000; Jiggins et al . 2001; Funk et al . 2002, 2006; Bradshaw & Schemske 2003; Rundle & Nosil 2005; and Funk, this volume, for review), and processes such as resource competition and predation are now known to be involved (Mallet & Barton 1989; Schluter 1994; Rundle et al . 2003; Vamosi 2005; Nosil & Crespi 2006a).
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".