Data from: Convergence in organ size but not energy metabolism enzyme activities among wild Lake Whitefish (Coregonus clupeaformis) species pairs
Bibliographic record
Abstract
The repeated evolution of similar phenotypes by similar mechanisms can be indicative of local adaptation, constraints or biases in the evolutionary process. Little is known about the incidence of physiological convergence in natural populations, so here we test whether energy metabolism in ‘dwarf’ and ‘normal’ Lake Whitefish evolves by similar mechanisms. Prior genomic and transcriptomic studies have found that divergence in energy metabolism is key to local adaptation in whitefish species pairs, but that distinct genetic and transcriptomic changes often underlie phenotypic evolution among lakes. Here, we predicted that traits at higher levels of biological organization, including the activities of energy metabolism enzymes (the product of enzyme concentration and turnover rate) and the relative proportions of metabolically active tissues (heart, liver, skeletal muscle), would show greater convergence than genetic and transcriptomic variation. We compared four whitefish species pairs and found convergence in organ size whereby all dwarf whitefish populations have a higher proportion of red skeletal muscle, three have relatively larger livers and two have relatively larger ventricles than normal fish. On the other hand, hepatic and muscle enzyme activities showed little convergence and were largely dependent on lake of origin. Only the most genetically divergent species pair (Cliff Lake) displayed white muscle enzyme activities matching results from laboratory-reared normal and dwarf whitefish. Overall, these data show convergence in the evolution of organ size, but not in the activities of candidate enzymes of energy metabolism, which may have evolved mainly as a consequence of demographic or ecological differences among lakes.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".