Membrane bioreactor treatment of commonly used organophosphate pesticides
Bibliographic record
Abstract
Five pesticide formulations registered for use in Canada containing organophosphate-insecticide active ingredients azinphos-methyl, chlorpyrifos, diazinon, malathion and phorate were subjected to treatment by membrane bioreactor (MBR) technology. The target active ingredients were introduced to the MBR at ppm level concentrations. The biodegradation of these compounds was analyzed daily using selected ion monitoring gas chromatography-mass spectrometry (GC/MS-SIM) following extraction of the analytes using solid-phase extraction (SPE). Amounts measuring 83 % to 98 % of the target analytes were removed with steady-state concentrations being reached within 5 days of their introduction. The dissolved oxygen, temperature, pH, and total heterotrophic bacterial population were monitored daily to ensure optimal conditions for biodegradation. The quality of the effluent from the MBR was assessed daily through spectrophotometric methods. Measurements were conducted for the concentration of ammonia, nitrate, nitrite, total and reactive phosphorus, as well as the chemical oxygen demand (COD) of the effluent. This study demonstrated that the MBR technology is feasible and efficient for treatment of organophosphate pesticides without introducing additional chemical additives.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".