Herbicide Residues in Water Resources: A Scoping Review
Bibliographic record
Abstract
Several recent studies have focused on leaching pesticides from agricultural soils into surface and groundwater resources during irrigation. As a result, information about herbicide residues in water was necessary for conserving related resources. This study provided an overview of monitoring herbicides in water resources worldwide. In this scoping review, five databases were searched for publications (1990 to April 2021), including Scopus, PubMed/Medline, Cochrane library, Embase, and Web of Science. Among the 394 identified articles, 17 papers were selected for inclusion. Most of these studies have been conducted in regions with low herbicide concentrations, including Spain, Greece, Canada, Brazil, Hungary, Malawi, Portugal, Lesotho, Germany, Serbia, and the USA. The high-level alachlor, metolachlor, atrazine, metribuzin, and simazine herbicides in groundwater were detected in Portugal (0.4-13μg/L). An overview of studies demonstrated that herbicides are widely used in water resources, and surface waters are more contaminated than groundwaters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".