<i>Streptococcus iniae</i>: an Emerging Pathogen in the Aquaculture Industry
Bibliographic record
Abstract
The aquaculture industry, which is increasingly being developed, has not yet been recognized to result in significant human disease. Aquaculture in North America involves diverse farming systems in diverse areas. The criticisms concern contamination of the environment by aquaculture systems through unwanted obstructions to coastal navigation, unsightly cages or pens, aquaculture effluents such as excess food and chemotherapeutics, and the use of nonnative species or native species that are either domesticated or genetically different from wild stocks. The level of contamination of aquaculture products with pathogenic bacteria depends on the environment and the bacteriological quality of the water where the fish are cultured. It should be noted that nonindigenous bacteria of fecal origin could be introduced into aquaculture ponds via contamination by birds and wild animals associated with farm waters. Streptococcus iniae has also been reported to be the causative agent of ongoing infection and excess mortality of tilapia in Texas aquaculture farms. Overcrowding in farms and during transport may have contributed to the increasing importance of streptococcal infections in fish. Finally, although S. iniae commonly colonized the surfaces of tilapia and other species of fish, isolates are genetically diverse. Although S. iniae is capable of causing invasive disease in humans, serious disease appears to be rare, and if people take the proper precautionary measures when handling whole, uncooked fish, infections caused by S. iniae can be prevented.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.010 |
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".