Quantitative PCR reveals transient and persistent algal viruses in Lake Ontario, Canada
Bibliographic record
Abstract
To determine if different algal viruses (Phycodnaviridae) share common patterns of seasonal abundance, quantitative PCR methods were developed and applied to monitor the abundances of three different viruses in Lake Ontario, Canada over 13 months. Throughout the year, the abundances of two different phycodnavirus polB gene fragments (LO1b-49 and LO1a-68) varied by more than two orders of magnitude, peaked during the autumn months, and were lowest during the summer. The seasonal abundance patterns of these two virus genes were similar and both were detected in almost every sample, but LO1b-49 was consistently an order of magnitude more abundant than LO1a-68. LO1b-49 reached a maximum abundance of 5413 +/- 312 genes ml(-1), whereas LO1a-68's abundance peaked at only 881 +/- 113 genes ml(-1). Another phycodnavirus polB fragment that was monitored (LO1b-16) was detected in only a few samples, but reached a higher maximum concentration (6771 +/- 879 genes ml(-1)) than either LO1b-49 or LO1a-68. The results of this year-long investigation of virus gene abundances suggests that Lake Ontario's phycodnavirus community is composed of persistent viruses detectable throughout the year and transient viruses present in only a few sporadic samples. The results also suggest that some persistent algal viruses are able to survive at relatively low abundances through several seasons.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".