Assessment and Assay Comparison for Detection of Antimicrobial Residues in Freshwater Aquaculture Fish in Erbil Governorate, Iraq
Bibliographic record
Abstract
The excessive and uncontrolled application of antibiotics in the fish farming industry, coupled with a lack of health monitoring and medication practices, is a driving force behind the escalating development of antimicrobial resistance. The present study assessed and compared qualitative field diffusion (QFD) and disk diffusion (DD) assays for the detection of antimicrobial residues (ARs) in diverse freshwater aquaculture fish. A total of 380 freshwater aquaculture fish (160 fresh and 180 frozen) samples were systematically collected between January and June 2021 from various retail stores located in Erbil Governorate, Iraq. Based on QFDA results, overall, ARs were detected (52; 15.3%) at a relatively lower frequency with comparatively higher frequency (21; 31.1%) in fresh than (31; 17.2%) frozen fish samples. On the other hand, DDA also revealed a comparable (45; 13.2%) prevalence rate of ARs. However, a low detection was observed more in fresh (17; 10.6%) than frozen (28; 15.6%) fish samples. Moreover, no statistically significant disparity (χ2 = 0.069; p = 0.79) between two assays and types of fish was recorded. In conclusion, the results of the present study showed that detecting a considerable frequency of ARs in these fish samples raises concerns about potential threats to public health. This underscores the necessity for understanding antibiotic application in aquaculture and its potential connection to antibiotic resistance in bacterial pathogens. Such comprehension is pivotal for formulating and implementing effective control and farm management strategies to address this pressing issue.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".