Aquatic virus culture collection: an absent (but necessary) safety net for environmental microbiologists
Bibliographic record
Abstract
Viruses are recognised as the most abundant biological entities on the planet. In addition to their role in disease, they are crucial components of co-evolutionary processes, are instrumental in global biogeochemical pathways such as carbon fluxes and nutrient recycling, and in some cases act regionally on climate processes. Importantly, viruses harbour an enormous, as of yet unexplored genetic and metabolic potential. Some viruses infecting microalgae harbour hundreds of genes, including genes involved in cellular metabolic pathways. Collectively, these attributes have given rise to new fields of research: environmental virology and viral ecology. While traditionally the potential of viruses was recognised by isolating novel viruses into culture and subsequent sequencing of their genomes in the laboratory, advancements in next-generation sequencing technologies now allow for direct sequencing of viral genomes from their natural setting, bypassing the need for culturing. Nevertheless, the lack of associated biological reference material with most of these novel environmental genomes is problematic as there are limitations to what can be achieved with sequence data alone. Where aquatic viruses do exist in culture, they are most often kept privately within research institutes and are not available to the wider research community. Many are thus at risk of being lost because research teams rarely have secure long term resources to ensure continued propagation. Culture collections do exist for medically and agriculturally important viruses causing disease, but collections focusing on viruses infecting aquatic algae and bacteria are non-existent. We therefore highlight here the need for a centralised depository for aquatic viruses and present arguments indicating the benefits such a collection would have for the scientific community of environmental virologists.
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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 | 0.045 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.007 | 0.015 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.028 | 0.074 |
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".