GENETICALLY BASED PHENOTYPE-ENVIRONMENT ASSOCIATION FOR SWIMMING BEHAVIOR IN LAKE WHITEFISH ECOTYPES (COREGONUS CLUPEAFORMIS MITCHILL)
Bibliographic record
Abstract
Studies of phenotype-environment associations in adaptive radiation have focused largely on morphological traits related to resource-based phenotypic differences. The genetic basis of adaptive behaviors implicated in population divergence remains poorly understood, as few studies have tested the hypothesis of behavioral phenotype-environment associations. We provide evidence of a phenotype-environment association for differential adaptive swimming behaviors through experiments conducted on dwarf, normal, and hybrid lake whitefish (Coregonus clupeaformis). Highly significant differences were observed for depth selection, directional changes, and burst swimming, implicating a genetic basis for these behaviors. Hybrid crosses revealed that depth selection is under additive genetic control, while dominance effects were suggested for directional changes and burst swimming. Estimates for the genetic basis of behavioral differentiation from an animal model were consistent with these observations. Comparative estimates of behavioral differentiation (Q(ST)) against neutral expectations (F(ST)) revealed pronounced departures from neutral expectations in all three behavioral phenotypes, consistent with the hypothesis that directional selection has driven the divergence of behavior in dwarf and normal lake whitefish ecotypes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".