Molecular phylogeny of sturgeon mimiviruses and Bayesian hierarchical modeling of their effect on wild Lake Sturgeon (Acipenser fulvescens) in Central Canada
Bibliographic record
Abstract
Sturgeon mimiviruses can cause a lethal disease of the integumentary systems of sturgeon (Acipenseridae). Here we provide phylogeographic evidence that sturgeon mimivirus is endemic in endangered populations of wild Lake Sturgeon within Canada's Hudson Bay drainage basin. Namao virus (NV) variants were diagnosed in 24% of Lake Sturgeon samples (n = 1329) collected between 2010–2015. Lake Sturgeon populations with the highest virus prevalence were from the Nelson River (58%) in 2015, Saskatchewan River (41%) in 2010 and South Saskatchewan River (36%) in 2011. Bayesian phylogenetic reconstructions suggested that four NV variants, designated HBDB I-IV, co-circulate temporally and spatially within and between the genetically and biogeographically distinct Lake Sturgeon populations. Evidence from recapture studies suggested that Lake Sturgeon across the basin are persistently infected with NV at prevalence and titer (103.6 equivalent plasmid copies per μg DNA) levels consistent with the hypothesis that wild Lake Sturgeon populations serve as a maintenance population and reservoir for sturgeon mimiviruses. Bayesian hierarchical modeling of NV in the Landing River population of Lake Sturgeon suggested that host weight and age were the best predictors of sturgeon mimivirus presence and titer, respectively, whereas water flow rate, level and temperature, and number of previous captures did not significantly improve model fit. A negative relationship was estimated between sturgeon mimivirus presence and Lake Sturgeon weight and between virus titer and Lake Sturgeon age.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".