EXPLORING POSSIBLE HUMAN INFLUENCES ON THE EVOLUTION OF DARWIN'S FINCHES
Bibliographic record
Abstract
Humans are an increasingly common influence on the evolution of natural populations. Potential arenas of influence include altered evolutionary trajectories within populations and modifications of the process of divergence among populations. We consider this second arena in the medium ground finch (Geospiza fortis) on Santa Cruz Island, Galápagos, Ecuador. Our study compared the G. fortis population at a relatively undisturbed site, El Garrapatero, to the population at a severely disturbed site, Academy Bay, which is immediately adjacent to the town of Puerto Ayora. The El Garrapatero population currently shows beak size bimodality that is tied to assortative mating and disruptive selection, whereas the Academy Bay population was historically bimodal but has lost this property in conjunction with a dramatic increase in local human population density. We here evaluate potential ecological-adaptive drivers of the differences in modality by quantifying relationships between morphology (beak and head dimensions), functional performance (bite force), and environmental characteristics (diet). Our main finding is that associations among these variables are generally weaker at Academy Bay than at El Garrapatero, possibly because novel foods are used at the former site irrespective of individual morphology and performance. These results are consistent with the hypothesis that the rugged adaptive landscapes promoting and maintaining diversification in nature can be smoothed by human activities, thus hindering ongoing adaptive radiation.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".