Pathogens and Passengers: Roles for Crustacean Zooplankton Viruses in the Global Ocean
Bibliographic record
Abstract
Viruses infect all living organisms, but the viruses of most marine animals are largely unknown. Crustacean zooplankton are a functional lynchpin in marine food webs, but very few have been interrogated for their associated viruses despite the profound potential effects of viral infection. Nonetheless, it is clear that the diversity of viruses in crustacean zooplankton is enormous, including members of all realms of RNA viruses, as well as single- and double-stranded DNA viruses, in many cases representing deep branches of viral evolution. As there is clear evidence that many of these viruses infect and replicate in zooplankton species, we posit that viral infection is likely responsible for a significant portion of unexplained non-consumptive mortality in this group. In turn, this infection affects food webs and alters biogeochemical cycling. In addition to the direct impacts of infection, zooplankton can vector economically devastating viruses of finfish and other crustaceans. The dissemination of these viruses is facilitated by the movement of zooplankton vertically between epi- and mesopelagic communities through seasonal and diel vertical migration (DVM) and across long distances in ship ballast water. The large potential impact of viruses on crustacean zooplankton emphasises the need to clearly establish the relationships between specific viruses and the zooplankton they infect and investigate disease and mortality for these host-virus pairs. Such data will enable investigations into a link between viral infection and seasonal dynamics of host populations. We are only beginning to uncover the diversity and function of viruses associated with crustacean zooplankton.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 teacher head, 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".