Hunting and evolution: theory, evidence, and unknowns
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
Intense selective harvest of large mammals who carry the largest weapons may lead to an evolutionary shrinkage of those weapons. Currently, evidence suggesting evolutionary effects of harvest is limited to a few species of Bovidae and only 1 study has obtained data indicating a genetic effect. To have an evolutionary impact, harvest must be intense, persistent over time, similar over a large area without an effective source of unselected immigrants, and remove large individuals before they have a chance to breed. Many current harvest schemes do not fulfill all of these requirements, and they are unlikely to cause evolution. Before changes in weapon size over time are attributed to evolution, potential environmental sources of change, mainly density and climate, must be considered. We suggest that the role of weapon size in determining reproductive success, especially in interaction with male age, will determine whether or not intensive selective harvests may have evolutionary consequences. Age at harvest is a very important variable to consider. Changes in age structure over time may reveal underlying changes in harvest pressure or selectivity. A lack of data hampers our ability to assess the potential evolutionary effects of selective hunting. We provide a list of research hypotheses required to advance our ability to assess the evolutionary sustainability of current management practices.
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 | 0.000 | 0.000 |
| 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 it