Multiplex PCR for species-level discrimination of yellow Lampmussel (Lampsilis cariosa (Say, 1817)) and Tidewater Mucket (Atlanticoncha ochracea)
Bibliographic record
Abstract
Effective management of imperiled freshwater mussel populations (Order Unionida) is dependent on accurate field identifications. Standard methods of identifying living mussels utilizing external shell characteristics, however, can be unreliable for some species given high levels of phenotypic plasticity and morphological overlap with other taxa. In Canada, Yellow Lampmussel (Lampsilis cariosa (Say, 1817)), a species of Special Concern, is limited in distribution to isolated populations within Nova Scotia and New Brunswick. Efforts to monitor this species can be complicated by difficulties in distinguishing Yellow Lampmussel from the Tidewater Mucket (Atlanticoncha ochracea (Say, 1817)). Both species are known from the Saint John River and its tributaries in New Brunswick and are also sympatric within the Sydney River watershed in Nova Scotia. In our survey of biology students and faculty at Cape Breton University, participants correctly identified these two species based on photographs of external shells only 61.7% of the time, with even the most experienced individuals achieving a success rate of just 68.8%. To facilitate species-level identification, here we have developed a simple genetic-based tool to differentiate between live Yellow Lampmussel and Tidewater Mucket. Using custom-designed primers and a single multiplex PCR reaction, the identity of these two species can be determined based on amplification product size. This tool should be of enormous value to freshwater mussel ecologists working to monitor Yellow Lampmussel populations and to explore other aspects of their biology and ecology.
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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) | 0.001 | 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) | 0.001 | 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".