The shape of things to come in the study of the origin of species?
Bibliographic record
Abstract
Perhaps Darwin would agree that speciation is no longer the mystery of mysteries that it used to be. It is now generally accepted that evolution by natural selection can contribute to ecological adaptation, resulting in the evolution of reproductive barriers and, hence, to the evolution of new species (Schluter & Conte 2009; Meyer 2011; Nosil 2012). From genes that encode silencing proteins that cause infertility in hybrid mice (Mihola et al. 2009), to segregation distorters linked to speciation in fruit flies (Phadnis & Orr 2009), or pollinator-mediated selection on flower colour alleles driving reinforcement in Texan wildflowers (Hopkins & Rausher 2012), characterization of the genes that drive speciation is providing clues to the origin of species (Nosil & Schluter 2011). It is becoming apparent that, while recent work continues to overturn historical ideas about sympatric speciation (e.g. Barluenga et al. 2006), ecological circumstances strongly influence patterns of genomic divergence, and ultimately the establishment of reproductive isolation when gene flow is present (Elmer & Meyer 2011). Less clear, however, are the genetic mechanisms that cause speciation, particularly when ongoing gene flow is occurring. Now, in this issue, Franchini et al. (2014) employ a classic genetic mapping approach augmented with new genomic tools to elucidate the genomic architecture of ecologically divergent body shapes in a pair of sympatric crater lake cichlid fishes. From over 450 segregating SNPs in an F2 cross, 72 SNPs were linked to 11 QTL associated with external morphology measured by means of traditional and geometric morphometrics. Annotation of two highly supported QTL further pointed to genes that might contribute to ecological divergence in body shape in Midas cichlids, overall supporting the hypothesis that genomic regions of large phenotypic effect may be contributing to early-stage divergence in Midas cichlids.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.026 |
| Scholarly communication | 0.010 | 0.041 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.008 | 0.016 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".