Effects of Waste Disposal: Temporo-Spatial Patterns of Bacterial Community Composition on Oyster Farms in Estuary Regions of Bahia, Brazil
Bibliographic record
Abstract
Oyster farming in Brazil is limited by a number of problems, such as lack of sanitary control in production areas. The aim of the present study was to use the multiple correspondence analyses (MCA) to correlate the microbiological quality of oyster production in the state of Bahia, Brazil, by linking it to environment parameters. Samples were randomly collected from 15 different oyster farms, from two different cultivation techniques (lantern and pillow baskets), corresponding dry and rainy periods. Twenty eight samples were collected, stored at 5 °C and send to the laboratory. The analyzed environmental parameters were pH, temperature, dissolved oxygen, salinity, and polluting sources like sewage outflow. Coliforms were counted at 35 °C and 45 °C: Escherichia coli, Salmonella sp., Pseudomonas sp. and Aeromonas sp. Statistical analyzes were performed using the data sizing reduction procedure (MCA), through the SPPS Program, version 18.0. A positive relationship was found between the dry period and lantern net farming with poor sanitary conditions. MCA allowed the analyzed commercial oyster farms to form three distinct groups, helped define the intervention strategies of these commercial oyster farms and provided the basis for implementing health protection measures.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".