Removal of large viruses and their dispersal through fecal pellets of the appendicularian<scp><i>Oikopleura dioica</i></scp>during<scp><i>Emiliania huxleyi</i></scp>bloom conditions
Bibliographic record
Abstract
Abstract Despite their importance in shaping the structure and function of marine microbial food webs, little is known about factors regulating marine virus abundance. Previous work demonstrated clearance of laboratory‐culturedEmiliania huxleyivirus by the appendicularianOikopleura dioica; however, the applicability of this interaction to natural virus assemblages was not investigated. Here, we conducted controlled laboratory experiments usingO. dioicaand mesocosm water containing natural virus assemblages with high densities of virus, and measured removal of virus byO. dioicausing both flow cytometry and molecular methods. Bayesian models based on flow cytometry quantification of virus particles demonstrated efficient removal of viruses (mean 90.3 mL ind−1d−1), with a clearance efficiency of 42.6% relative to food algae. Molecular detection of virus removal by quantification of viralmcpgene copies revealed a mean clearance rate of 68.1 mL ind−1d−1. Fecal pellets from these experiments demonstrated that viruses in fecal pellets retain infectivity despite passage through theO. dioicagut. Shotgun metavirome analysis demonstratedO. dioicaremoval of large virus groups, notably the Phycodnaviridae. The results demonstrate the removal ofE. huxleyivirus from natural virus assemblages byO. dioicaand the maintenance of viral infectivity when incorporated into fecal pellets, prompting further investigation on the fate of fecal‐packaged viruses and their impact on host dynamics. Furthermore, our results indicate the generality of this interaction for other large algal viruses, raising questions about the implications of this mechanism of marine virus redistribution on the broader marine virus community.
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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) | 0.001 | 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".