Bibliographic record
Abstract
Wild populations must continuously respond to environmental changes or they risk extinction. Those responses can be measured as phenotypic rates of change, which can allow us to predict contemporary adaptive responses, some of which are evolutionary. About two decades ago, a database of phenotypic rates of change in wild populations was compiled. Since then, researchers have used (and expanded) this database to examine phenotypic responses to specific types of human disturbance. Here, we update the database by adding 5675 new estimates of phenotypic change. Using this newer version of the data base, now containing 7338 estimates of phenotypic change, we revisit the conclusions of four published articles. We then synthesize the expanded database to compare rates of change across different types of human disturbance. Analyses of this expanded database suggest that: (i) a small absolute difference in rates of change exists between human disturbed and natural populations, (ii) harvesting by humans results in higher rates of change than other types of disturbance, (iii) introduced populations have increased rates of change, and (iv) body size does not increase through time. Thus, findings from earlier analyses have largely held-up in analyses of our new database that encompass a much larger breadth of species, traits, and human disturbances. Lastly, we use new analyses to explore how various 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 phenotypic change.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".