Human influences on evolution, and the ecological and societal consequences
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Humans have dramatic, diverse and far-reaching influences on the evolution of other organisms. Numerous examples of this human-induced contemporary evolution have been reported in a number of 'contexts', including hunting, harvesting, fishing, agriculture, medicine, climate change, pollution, eutrophication, urbanization, habitat fragmentation, biological invasions and emerging/disappearing diseases. Although numerous papers, journal special issues and books have addressed each of these contexts individually, the time has come to consider them together and thereby seek important similarities and differences. The goal of this special issue, and this introductory paper, is to promote and expand this nascent integration. We first develop predictions as to which human contexts might cause the strongest and most consistent directional selection, the greatest changes in evolutionary potential, the greatest genetic (as opposed to plastic) changes and the greatest effects on evolutionary diversification We then develop predictions as to the contexts where human-induced evolutionary changes might have the strongest effects on the population dynamics of the focal evolving species, the structure of their communities, the functions of their ecosystems and the benefits and costs for human societies. These qualitative predictions are intended as a rallying point for broader and more detailed future discussions of how human influences shape evolution, and how that evolution then influences species traits, biodiversity, ecosystems and humans.This article is part of the themed issue 'Human influences on evolution, and the ecological and societal consequences'.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.012 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it