Insights into kinetic and regression models developed to estimate the abundance of antibiotic-resistant genes during biological digestion of wastewater sludge
Bibliographic record
Abstract
Wastewater treatment plants are hubs of antibiotic-resistant genes (ARGs). During wastewater treatment, ARGs accumulate in wastewater sludge and some survive biological digestion. After land application of digested sludge, ARGs are transported to soil, water, and air, and may encounter humans and animals. ARGs are typically quantified by quantitative polymerase chain reaction (qPCR) on isolated DNA. Nevertheless, DNA isolation and qPCR are time-consuming, expensive, and prone to contamination. Therefore, there is a need to estimate ARGs quantities via methods that can be readily employed. Such estimation would help to protect public health via modifying biological digestion to maximize the removal of ARGs. Two approaches that make such estimation are kinetic and regression modeling. The kinetic models have been mainly of the first order. This review examines the application of the kinetic models to estimate the abundance of ARGs during biological sludge digestion. It also discusses how biological sludge digesters can be designed using kinetic models. The literature provides single and multiple regression models, from which an ARGs -Solids -Nutrients nexus, a focal point of this review, is inferred. This review demonstrates that regression models are mathematical expressions of that nexus. Also, existing challenges are highlighted and suggestions for future are provided.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".