A fine‐scale phylogenetic assessment of digenean trematodes in central Alberta reveals we have yet to uncover their total diversity
Bibliographic record
Abstract
Despite over 100 years of digenean trematode parasite species descriptions, from a wide diversity of vertebrate and invertebrate host species, our ability to recognize the diversity of trematode species within a single lake remains an incredible challenge. The most challenging aspect is the identification of species from larval stages derived from intermediate hosts, due to the disjointed data of adult worm morphological descriptions, from which species are named, and links to corresponding molecular identifiers in depauperate databases. Cryptic species also play a significant role in the challenge of linking trematode larvae to adults, species identifications, and estimating diversity. Herein, we utilize a large, longitudinal dataset of snail first-intermediate host infection data from lakes in Alberta, Canada, to infer trematode larval diversity using molecular phylogenetics and snail host associations. From our assessments, we uncover a diversity of 79 larval trematode species among just five snail host species. Only 14 species were identified to a previously described species, while the other 65 species are either cryptic or otherwise unrepresented by mitochondrial genes in GenBank. This study currently represents the largest and most diverse singular molecular survey of trematode larval fauna composed of over one thousand mitochondrial sequences. Surprisingly, rarefaction analyses indicate we have yet to capture the complete diversity of trematodes from our sampling area.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".