Colonization and release processes of viruses and prokaryotes on artificial marine macroaggregates
Bibliographic record
Abstract
Marine organic aggregates are sites of high of viral accumulation; however, still little is known about their colonization processes and interactions with their local bacterial hosts. By taking advantage of a novel approach (paramagnetic functionalized microsphere method) to create and incubate artificial macroaggregates, we examined the small-scale movements of viruses and bacteria between such marine snow particles and the surrounding water. The examination of the codynamics of both free-living and attached viral and bacterial abundance, over 12 hours of incubation in virus-free water, suggests that aggregates are rather comparable to viral factories than to viral traps where a significant part of the virions production might be locally diverted to the water column. Also, the near-zero proportion of lysogenized cells measured in aggregates after mitomycin-C induction seems to indicate that lysogeny is not a prominent viral reproduction pathway in organic aggregates where most viruses might rather be virulent. Finally, we hypothesize that, contrary to bacteria, which can use both strong attachment and detachment from aggregates (two-way motion of bacteria), the adsorption of planktonic viruses appears to be numerically negligible compared to their massive export from the aggregates into the water column (one-way motion of viruses).
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".