Organochlorine pesticides remediation techniques: Technological perspective and opportunities
Bibliographic record
Abstract
Organochlorine pesticides have been widely used in agriculture to control agricultural pests. Although effective in controlling pests, organochlorine pesticides present numerous hazards to ecosystems and human health due to their persistence in the environment, bioaccumulation, and toxicity. Scientific studies have shown that organochlorines may be associated with endocrine and neurological problems. Several strategies have been developed to apply treatment techniques to remove pesticides from various ecosystems, both aquatic and terrestrial. Physicochemical and biological methods have revealed other potentialities for remediation of aqueous environments contaminated with organochlorine pesticides. In addition, combined processes using different approaches have been highlighted as efficient alternatives to mitigate the impacts of agrochemicals on the environment, e.g., physical technique followed by a biological process. However, there are still numerous gaps that need to be explored and elucidated. Therefore, this review addressed the impacts of organochlorine pesticides on ecosystems and some treatment techniques used to remove agrochemicals from water. Furthermore, new findings, technological perspectives, and opportunities on this subject were presented and discussed.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".