Complex seasonality observed amongst diverse phytoplankton viruses in the <scp>B</scp>ay of <scp>Q</scp>uinte, an embayment of <scp>L</scp>ake <scp>O</scp>ntario
Bibliographic record
Abstract
Summary To initiate research on algal viruses (viruses that infect eukaryotic algae) and cyanophages (viruses that infect cyanobacteria) in the Bay of Quinte, a Lake Ontario embayment, samples of viruses free in the water (i.e. not associated with particulate material) were collected throughout 2011 with the goals of examining the diversity of phytoplankton viruses and monitoring their dynamics. PCR and sequencing of DNA polymerase (polB) and major capsid protein (MCP) genes from algal viruses, and sheath protein genes from cyanophages, revealed diverse phytoplankton viruses in the bay. Specifically, polB sequences from the bay were most closely related to sequences from viruses that infect prasinophyte algae, MCP sequences were related to sequences from viruses that infect prasinophytes and from Mimivirus‐like viruses that infect prymnesiophytes and prasinophytes, whilst sheath protein sequences were related to sequences from the phage Ma‐LMM01 that infects M. aeruginosa. The abundances of 10 distinct viral genes monitored using quantitative PCR ranged from exceptionally high values (e.g. 256 441 gene copies mL−1 for a putative M. aeruginosa phage) to values that were just above detection limits (e.g. a putative Prasinovirus never exceeded 20 gene copies mL−1). Patterns of abundance included genes that were seasonally sporadic or geographically patchy, as well as some that were stable throughout the bay over the entire year. Despite the heterogeneity of viral abundance across the bay, gene abundance clustered by sampling date and geographical location. Even for closely related viruses, seasonality and geographical distribution were distinct. By providing evidence for the complex seasonality of diverse phytoplankton viruses, this work highlighted significant gaps in knowledge of aquatic virus ecology that can be extrapolated from this one system to most aquatic environments.
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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 | 0.001 | 0.000 |
| 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".