Estimated prevalence of Aerococcus viridans and Anophryoides haemophila in American lobsters Homarus americanus freshly captured in the waters of Prince Edward Island, Canada
Bibliographic record
Abstract
The Canadian lobster industry holds lobsters Homarus americanus in captivity for various periods to supply markets with live product year-round. Mortality during holding results in considerable losses, estimated at 10 to 15 % yr(-1) by the industry. This study examined the prevalence of Anophryoides haemophila and Aerococcus viridans, causative agents of 'bumper car' disease and gaffkemia, respectively, in lobsters freshly captured in the waters of Prince Edward Island during the spring and fall fishing seasons of 1997. A total of 116 lobsters were sampled in the spring, and 138 in the fall. A. haemophila was not detected in the spring, while the prevalence was 0.72 % in the fall with a 95% confidence interval (CI) of 0.02 to 3.97% and an overall prevalence of 0.39% (95% CI: 0.01 to 2.17%). The prevalence of A. viridans was estimated at 6.9% (95% CI: 3.0 to 13.14%) in the spring, 5.8% in the fall (95% CI: 2.54 to 11.10%), and 6.30% overall (95% CI: 3.64 to 10.03%). Because of the reduced interest in food of diseased lobsters, and compromised metabolism in the case of gaffkemia, these prevalence estimates are likely underestimates of the true prevalence of gaffkemia and 'bumper car' disease in the wild populations of lobster around Prince Edward Island.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".