Identification of Freshwater <i>Phycodnaviridae</i> and Their Potential Phytoplankton Hosts, Using DNA <i>pol</i> Sequence Fragments and a Genetic-Distance Analysis
Bibliographic record
Abstract
Viruses that infect phytoplankton are an important component of aquatic ecosystems, yet in lakes they remain largely unstudied. In order to investigate viruses (Phycodnaviridae) infecting eukaryotic phytoplankton in lakes and to estimate the number of potential host species, samples were collected from four lakes at the Experimental Lakes Area in Ontario, Canada, during the ice-free period (mid-May to mid-October) of 2004. From each lake, Phycodnaviridae DNA polymerase (pol) gene fragments were amplified using algal-virus-specific primers and separated by denaturing gradient gel electrophoresis; 20 bands were extracted from the gels and sequenced. Phylogenetic analysis indicated that freshwater environmental phycodnavirus sequences belong to distinct phylogenetic groups. An analysis of the genetic distances "within" and "between" monophyletic groups of phycodnavirus isolates indicated that DNA pol sequences that differed by more than 7% at the inferred amino acid level were from viruses that infect different host species. Application of this threshold to phylogenies of environmental sequences indicated that the DNA pol sequences from these lakes came from viruses that infect at least nine different phytoplankton species. A multivariate statistical analysis suggested that potential freshwater hosts included Mallomonas sp., Monoraphidium sp., and Cyclotella sp. This approach should help to unravel the relationships between viruses in the environment and the phytoplankton hosts they infect.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".