The evolution of the major histocompatibility complex in upstream versus downstream river populations of the longnose dace
Bibliographic record
Abstract
Abstract Populations in upstream versus downstream river locations can be exposed to vastly different environmental and ecological conditions and can thus harbor different genetic resources due to selection and neutral processes. An interesting question is how upstream–downstream directionality in rivers affects the evolution of immune response genes. We used next‐generation amplicon sequencing to identify eight alleles of the major histocompatibility complex (MHC) class II β exon 2 in the cyprinid longnose dace (Rhinichthys cataractae) from three rivers in Alberta, upstream and downstream of municipal and agricultural areas along contaminant gradients. We used these data to test for directional and balancing selection on the MHC. We also genotyped microsatellite loci to examine neutral population processes in this system. We found evidence for balancing selection on the MHC in the form of increased nonsynonymous variation relative to neutral expectations, and selection occurred at more amino acid residues upstream than downstream in two rivers. We found this pattern despite no population structure or isolation by distance, based on microsatellite data, at these sites. Overall, our results suggest that MHC evolution is driven by upstream–downstream directionality in fish inhabiting this system.
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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 | 0.000 |
| 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 | 0.000 |
| 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 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".