A universal empirical equation to estimate the abundance of carbapenem-resistant genes during aerobic digestion of wastewater sludge
Bibliographic record
Abstract
ABSTRACT Carbapenem-resistant genes (CRGs) exist in wastewater and accumulate in wastewater sludge. Due to the potential threat posed by the CRGs, it is important to quantify CRGs and predict their removal and discharge concentrations during aerobic sludge digestion. Nonetheless, gene quantification is tedious, error-prone and expensive. This study aims to develop multiple regression models to estimate CRGs from sludge parameters that are routinely measured for the monitoring and design of aerobic sludge digesters. Batch reactors were operated at mesophilic and thermophilic temperatures for 20-35 days. Sludge samples were periodically taken during aerobic digestion. Three CRGs (blaGES, blaOXA-48 and blaIMP-27) together with 16S rRNA and integron class 1 genes were quantified. Aerobic digestion reduced the abundance of all target genes. Multiple regression modelling was conducted in linear (LM) and non-linear (NLM) modes. Sums of squared errors of the LM models were 0-0.048, whereas those of the NLM models were 0–0.003. Adjusted R2 ranges of the LM and NLM models were 0.774–0.931 and 0.986–1, respectively. Overall, the NLM models predicted the abundance of target genes more accurately than the LM models. NLM models may be used to modify the design and operational parameters of aerobic sludge digesters.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".