DIVERGENT SELECTION DRIVES THE ADAPTIVE RADIATION OF CROSSBILLS
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
Knowledge of how phenotype influences fitness is necessary if we are to understand the basis of natural selection and how natural selection contributes to adaptive radiations. Here I quantify selection on a wild population of red crossbills (Loxia curvirostra complex) in the South Hills, Idaho. Bill depth is the target of selection and selection on bill depth is stabilizing. I then show how fitness is related to both bill depth and performance. I use these and previously published relationships to estimate a fitness surface for five species of red crossbills that are part of an ongoing adaptive radiation in western North America. The fitness surface for crossbills has distinct peaks and valleys, with each crossbill species residing on or very near the summits. This work strongly supports a key tenet of the ecological theory of adaptive radiations; namely, divergent selection for utilizing alternative resources is the ultimate cause of adaptive radiations.
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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) | 0.015 | 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