“Quasigenus” among <i>Phycodnaviridae</i> : A diversity of chlorophyte-infecting viruses in response to a dense algal culture in a high-rate algal pond
Bibliographic record
Abstract
Abstract This study approaches a high rate algal pond (HRAP) culture by metagenomic sequencing of the viral DNA fraction, this includes the so-called giant virus fraction (phylum Nucleocytoviricota ), with the goal of revealing viruses coexisting within an intensified algal culture. A wealth of interesting novel viruses is revealed, including members of Nucleocytoviricota, Lavidaviridae , and polinton-like viruses, which are taxa containing previously characterized algal viruses. Our sequencing results are coupled with a virus targeted qPCR study and 18S rDNA metabarcoding to elucidate potential virus-host interactions. Several species of green algae are identified (Chlorophyta), likely representing the alternating dominant populations during the year of study. Finally, we observe a bloom of viral diversity within the family Phycodnaviridae ( Nucleocytoviricota ), including highly related but non-identical genotypes, appearing in the HRAP in September and October 2018. This bloom is most likely the cause of a mass mortality event of the cultured algae that occurred during these same months. We hypothesize that these related Phycodnaviridae lineages selectively infect different strains of the same algal species of the Genus Picochlorum that have been identified in the HRAP by metabarcoding and coined this phenomenon a “quasigenus” by analogy to the RNA virus quasispecies concept.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".