Post-Glacial Dispersal Patterns of Pyganodon grandis (Bivalvia: Unionidae) Into the Lower Great Lakes Watershed
Bibliographic record
Abstract
Pyganodon grandis (Say, 1829) and other unionid mussels arrived in the Laurentian Great Lakes only after the last glacial maximum in North America. The mussel assemblage is thought to have entered Lake Erie as a wave of expansion from west to east, and moved upstream within tributary rivers. A similar process for most mussels occurred in Lake Ontario, but by eastern species, as the two lake systems are separated by Niagara Falls. Only P. grandis is abundant in both lakes. We applied variation in a fragment of the mtDNA Carboxylase I (COI) gene to identify potential historical paths for P. grandis to enter these watersheds. Nearly complete monomorphy characterized the Lake Ontario population for the most common Lake Erie allele, which is concordant with sequential founder effects across the Great Lakes. A ?st including populations from both Lakes, the intervening Niagara River, tributaries of Lake Erie, and additional samples from below the Laurentian Divide, was 0.10. The southern tributaries of Lake Erie had greater genetic diversity, although few haplotypes were shared among regions or even neighboring streams. Tributary and lake populations differed significantly, yet variation across this divide was not significant. This pattern likely arose from stream capture of upper Ohio River tributaries that once flowed south, and then population isolation, with upstream dispersal from Lake Erie limited to the lower reaches.
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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 | 0.000 | 0.000 |
| 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".