Bibliographic record
Abstract
Urban evolutionary biology is the study of rapid evolutionary change in response to humans and our use of lands to support city dwellers. Because cities are relatively modern additions to the natural world, research on urban evolution tends to focus on microevolutionary change that has happened across a few to many hundreds of generations. These questions still fall under the broad purview of evolutionary ecology. But the severity, rapidity, and replication of environmental changes that drive evolution in this context make it worthy of specific attention. Urban evolution provides the opportunity to study the earliest stages of evolution in a context that is scientifically interesting and societally important. The newness of urban populations and their proximity to natural populations also creates challenges when trying to detect population genetic change. In a From the Cover article in this issue of Molecular Ecology, Mueller et al. (2020) use whole genome resequencing data to address some of these challenges while exploring genetic changes associated with urbanization in 3 replicate urban-rural burrowing owl (Athene cunicularia) populations. Combining multiple approaches across these sample sites Mueller et al. find evidence for selection on genes whose function is related to synapses, neuron projections, brain connectivity, and cognitive function in general. That selection was parallel suggests brain processes were likely important for urban adaptation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".