<i>Streptococcus iniae</i>: an Emerging Pathogen in the Aquaculture Industry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| 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 it