Large Scale Nationwide Screening of Bioavailable Tetracyclines and Arsenic Using Whole-cell Bioreporter from Pangasius and Tilapia Aquaculture System in Bangladesh
Bibliographic record
Abstract
In this study, a large-scale screening of bioavailable tetracycline (TC) and arsenic (As) was carried out using Whole-Cell biosensor bacteria from the pond surface waters of selected pangasius and tilapia farms across Bangladesh. Bioavailability of Tetracyclines and Arsenic was detected using biosensor bacteria E. coli K12 pTetLux1 and E. coli DH5R (pJAMA-arsR), respectively. 62 samples were within the tetracycline detection limit; others were below the detection limit. The highest concentration 31.27µg/l was found in a pond of Jashore and the lowest concentration 2.57µg/l, was measured from Trishal, Mymensingh. Tetracyclines were detected in 19.57 % of all analyzed samples and varied from below the detection limit to a maximum of 31.27 μg/l. Khulna showed the maximum percentage of detected samples (90%), followed by Mymensingh (52%), Cumilla (27.5%), Jashore (14%), Bogura (14%), and Sathkhira (10%). Bioavailable As were detected in only 5.7% of all analyzed samples. Detectable as samples were higher in number among the collected samples at Madaripur and higher in percentage at Sathkhira (30%) followed by Madaripur (20%), Cumilla (5%), and Gopalgonj (3.2%). The pick level of Arsenic was found in Madaripur, which was 9.45 µg/l and the lowest figure was 2.35 µg/l, found in Cumilla. Though the concentrations of arsenic in groundwater (tube well water) were high in all studied regions, the concentrations of arsenic in pond water were low in every region. The concentration of as in the experimental ponds was neither very high nor in the danger limit. Use of shallow tube well water, fertilizers, drugs, pesticides, and herbicides possessed with as during fish culture might be the source of as contamination. However, extensive use of antibiotics results in the emergence and spread of antibiotic resistance in the aquatic environment.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".