Lack of genetic population structure of slimy sculpin in a large, fragmented lake
Bibliographic record
Abstract
Abstract Most of what is known about sculpin population structure comes from research in streams; however, slimy sculpins are also a common benthic species in deep lakes. In streams, sculpins are considered to be a relatively inactive species, moving only small distances, and characteristically have high levels of genetic structure. We examined population genetic structure of slimy sculpin (Cottus cognatus) across multiple barriers and over distances up to 227 km in Lake Champlain (USA, Canada) and Lake Ontario (USA, Canada) to determine whether lake populations of sculpin are also highly structured. We predicted that slimy sculpin populations in Lake Champlain would be structured by six causeways as well as by distance, Lake Ontario populations would be structured only by distance, and differences between the lakes would be large relative to within‐lake differences. We examined microsatellite variation among 200 slimy sculpins from Lake Champlain and 48 slimy sculpins from Lake Ontario to evaluate patterns of population connectivity and structure. There was no indication of population substructuring within either lake but sculpin were genetically distinct between lakes. We conclude that there is a single, panmictic population of sculpin present in Lake Champlain and another potentially panmictic population in Lake Ontario, with no indication of genetic isolation by distance. Our results contrast with data from sculpin in streams, suggesting distance and habitat fragmentation exert little influence on population connectivity of benthic fish in 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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.000 |
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".