The Pace of Modern Life, Revisited
Bibliographic record
Abstract
ABSTRACT Wild populations must continuously adapt to environmental changes or they risk extinction. Such adaptations can be measured as phenotypic rates of change and can allow us to predict patterns of contemporary evolutionary change. About two decades ago, a dataset of phenotypic rates of change in wild populations was compiled. Since then, researchers have used (and expanded) this dataset to look at microevolutionary processes in relation to specific types of human disturbances. Here, we have updated the dataset adding 5675 estimates of phenotypic changes and used it to revisit established patterns of contemporary evolutionary change. Using this newer version, containing 7338 estimates of phenotypic changes, we revisit the conclusions of four published articles. We then synthesize the expanded dataset to compare rates of change across different types of human disturbance. Analyses of this expanded dataset suggests that: I. a small absolute difference in rates of change exists between human disturbed and natural populations, II. harvesting by humans results in larger rates of change than other types of disturbances, III. introduced populations have increased rates of change, and IV. body size does not increase through time. Overall, findings from earlier analyses have largely held-up in analyses of our new dataset that encompass a much larger breadth of species, traits, and human disturbances. Lastly, we found that types of human disturbances affect rates of phenotypic change, and we call for this database to serve as a steppingstone for further analyses to understand patterns of contemporary 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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".