DNA Barcode Identification of Freshwater Snails in the Family Bithyniidae from Thailand
Bibliographic record
Abstract
Freshwater snails in the family Bithyniidae are the first intermediate host for Southeast Asian liver fluke (Opisthorchis viverrini), the causative agent of opisthorchiasis. Unfortunately, the subtle morphological characters that differentiate species in this group are not easily discerned by non-specialists. This is a serious matter because the identification of bithyniid species is a fundamental prerequisite for better understanding of the epidemiology of this disease. Because DNA barcoding, the analysis of sequence diversity in the 5' region of the mitochondrial COI gene, has shown strong performance in other taxonomic groups, we decided to test its capacity to resolve 10 species/ subspecies of bithyniids from Thailand. Our analysis of 217 specimens indicated that COI sequences delivered species-level identification for 9 of 10 currently recognized species. The mean intraspecific divergence of COI was 2.3% (range 0-9.2 %), whereas sequence divergences between congeneric species averaged 8.7% (range 0-22.2 %). Although our results indicate that DNA barcoding can differentiate species of these medically-important snails, we also detected evidence for the presence of one overlooked species and one possible case of synonymy.
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 | 0.000 | 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 | 0.000 |
| 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".