A Black Swan Event Drives Eco-Evolutionary Heterogeneity
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
Environmental variation is a constant. Difficult to predict but important ‘Black Swan’ events are increasing in frequency and magnitude, but we are only beginning to understand the ecological and evolutionary consequences of such events. Extreme events can increase or decrease eco-evolutionary heterogeneity depending on the spatial grain at which they occur. Here I present a 6-year study of 3000+ individual univoltine gall makers and their enemies from 15 populations. An extreme event in one generation homogenized a key environmental determinant of enemy attack rates and survival, but exposed gall makers to an alternative environmental driver of ecological interactions. Counterintuitively, rather than acting as an ecological or evolutionary filter, extreme events can create greater spatial variation in demography, species interactions, natural selection, and evolutionary change. I suggest that the eco-evolutionary consequences of Black Swan events can only be understood by considering the evolutionary outcome of what are often complex species interactions.
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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) | 0.001 | 0.002 |
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