A proposed method for analyzing molecular signatures to detect hermaphroditism in freshwater mussels: a case study using the eastern floater (<i>Pyganodon cataracta</i>)
Bibliographic record
Abstract
Many freshwater mussels (order Unionida) have an unusual system of doubly uniparental inheritance (DUI) of mitochondrial (mt) DNA. In species with DUI, males possess a female-transmitted (F-type) mt genome and a male-transmitted (M-type) mt genome. These genomes contain non-canonical open reading frame (orf) genes, referred to as f-orf and m-orf, present in F and M mt genomes, respectively. These genes have been implicated in sexual development in Unionida. When gonochoric species become hermaphroditic, which has happened several times in Unionida, they lose their M-type mt genome and f-orf genes evolve dramatically. Resulting F-ORF proteins are highly divergent in terms of primary nucleotide sequence, inferred amino acids, and hydrophobic properties; these genes (and proteins) are referred to as hermaphroditic orfs or h-orfs (and H-ORFs). We investigated patterns of hydrophobicity divergence for H-ORF proteins in hermaphrodites versus F-ORF proteins in closely related gonochoric species against cytochrome c oxidase subunit 1 (cox1) divergences. This approach was used to assess whether cryptic hermaphrodites can be detected. Although we did not detect evidence for the recent transition of any populations of eastern floaters (Pyganodon cataracta (Say, 1817)) to hermaphroditism, our analyses demonstrate that molecular signatures in mtDNA can be used to detect hermaphroditism in freshwater mussels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".