Urban evolution comes into its own: Emerging themes and future directions of a burgeoning field
Bibliographic record
Abstract
Urbanization has recently emerged as an exciting new direction for evolutionary research founded on our growing understanding of rapid evolution paired with the expansion of novel urban habitats. Urbanization can influence adaptive and nonadaptive evolution in urban-dwelling species, but generalized patterns and the predictability of urban evolutionary responses within populations remain unclear. This editorial introduces the special feature "Evolution in Urban Environments" and addresses four major emerging themes, which include: (a) adaptive evolution and phenotypic plasticity via physiological responses to urban climate, (b) adaptive evolution via phenotype-environment relationships in urban habitats, (c) population connectivity and genetic drift in urban landscapes, and (d) human-wildlife interactions in urban spaces. Here, we present the 16 articles (12 empirical, 3 review, 1 capstone) within this issue and how they represent each of these four emerging themes in urban evolutionary biology. Finally, we discuss how these articles address previous questions and have now raised new ones, highlighting important new directions for the field.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| 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".