Bibliographic record
Abstract
Urbanization represents a dominant and growing form of disturbance to Earth's natural ecosystems, affecting biodiversity and ecosystem services on a global scale. While decades of research have illuminated the effects of urban environmental change on the structure and function of ecological communities in cities, only recently have researchers begun exploring the effects of urbanization on the evolution of urban populations. The 15 articles in this special feature represent the leading edge of urban evolutionary biology and address existing gaps in our knowledge. These gaps include: (i) the absence of theoretical models examining how multiple evolutionary mechanisms interact to affect evolution in urban environments; (ii) a lack of data on how urbanization affects natural selection and local adaptation; (iii) poor understanding of whether urban areas consistently affect non-adaptive and adaptive evolution in similar ways across multiple cities; (iv) insufficient data on the genetic and especially genomic signatures of urban evolutionary change; and (v) limited understanding of the evolutionary processes underlying the origin of new human commensals. Using theory, observations from natural populations, common gardens, genomic data and cutting-edge population genomic and landscape genetic tools, the papers in this special feature address these gaps and highlight the power of urban evolutionary biology as a globally replicated 'experiment' that provides a powerful approach for understanding how human altered environments affect evolution.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".