PaV1 infection in the Florida spiny lobster (<i>Panulirus argus</i>) fishery and its effects on trap function and disease transmission
Bibliographic record
Abstract
The Caribbean spiny lobster (Panulirus argus) supports the most economically valuable fishery in the Caribbean. In Florida, USA, the majority of the catch is landed in traps “baited” with live, sublegal-sized lobsters that attract other lobsters due to their social nature. This species is also commonly infected by the pathogenic virus Panulirus argus Virus 1 (PaV1). Here we describe a polymerase chain reaction (PCR) based assessment of the prevalence of PaV1 in the lobster fishery from the Florida Keys. We tested the effect of PaV1-infected lobsters in traps on catch and on transmission to other trapped, uninfected lobsters. We found that 11% of the lobsters caught in commercial traps were positive for the virus by PCR, but none of these animals showed visible signs of disease. We also tested whether healthy lobsters avoid diseased lobsters in traps. Traps into which we introduced an infected lobster caught significantly fewer lobsters than traps containing an uninfected lobster. Moreover, uninfected lobsters confined in traps with infected lobsters acquired significantly more PaV1 infections than those confined with uninfected lobsters. This study demonstrates the indirect effects that pathogens can have on fisheries and the unintended consequences of certain fishery practices on the epidemiology of a marine pathogen.
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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".