DETERMINANTS OF THE STRENGTH OF DISRUPTIVE AND/OR DIVERGENT SELECTION ARISING FROM RESOURCE COMPETITION
Bibliographic record
Abstract
We investigate how the intensity of competition for resources affects the strength of disruptive selection on a resource acquisition trait. This is done by analyzing several consumer-resource models in which consumers use a linear array of resources. We show that disruptive selection can be diminished under both strong and weak competition, making disruptive selection a unimodal function of the strength of competition. Weak selection under strong competition arises when competition causes the extinction (for self-reproducing resources) or depletion (for abiotic resources) of the most rapidly caught resources. Weak selection under weak competition is a consequence of minimal effects of consumers on resources. The precise relationship between intensity of competition and strength of disruptive selection is sensitive to the shape of the consumer's resource utilization curve and the nature of resource growth. The most strongly unimodal competition-selection relationships result from utilization curves with long tails. Our results show that a simple comparison of the width of the resource abundance distribution and the consumer's utilization function is not sufficient to determine whether selection is disruptive. The results may explain some contradictory experimental findings regarding the effect of consumer mortality on the strength of disruptive selection.
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".