Virus et prophages dans les écosystèmes aquatiques
Bibliographic record
Abstract
In this review, available data on the structure (diversity, abundance, biomass) and functional imprints (bacteriolysis, lysogeny, gene transfers, regulation of prokaryotic diversity) of natural viruses in the context of food webs in aquatic microbial ecology, and the related biogeochemical cycles, are summarized. Viruses are the most abundant, and probably the most diverse, biological entities in aquatic ecosystems and in the biosphere (i.e., viriosphere). Aquatic viruses typically exceed 107 particles/mL in mesotrophic conditions, the majority being represented by phages without tails and by tailed-phages such as members of the family Siphoviridae. Both types of phages have a small capsid and a small genome size, which is considered an evolutionary adaptation to planktonic life. Their contribution to microbial mortality is significant. There is strong evidence that phages exert a significant pressure on the community structure and diversity and on the diversification of potential hosts, mainly through two major pathways: biogeochemical catalysis from lysis products and horizontal gene transfers. In turn, phages are sensitive to environmental factors, both in terms of integrity and of infectivity. Some phages contain typical viral genes that code for biological functions of interest, such as photosynthesis. In general, development in viral ecology is a source of new knowledge for the scientific community in the domain of environmental sciences, but also in the context of evolutionary biology of living cellular organisms, the obligatory hosts for viruses. For example, the recent discovery of a giant virus that becomes ill through infection by another virus (i.e., a viriophage) is fuelling debate about whether viruses are alive. Finally, future research directions are identified in the context of general aquatic ecology, including ecological researches on cyanophages and other phytoplanktonic phages as a priority, primarily in freshwater lakes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".