Herbicide Residues in Biota, Analysis of
Bibliographic record
Abstract
Abstract Herbicides are used in agriculture to increase productivity by controlling and inhibiting production of unwanted plants interfering with proper growth and nourishment of valuable crops. Anthropogenic activities have promoted widespread occurrence of herbicide residues in various biota resulting in concerns for animal (including human) and environmental health. This article examines current extraction, cleanup, and instrumental methods available for analyzing herbicide residues in biota, integrating multiresidue methods (MRMs) and miniaturization of apparatus to reduce solvent volumes required for sample extraction and analysis. Methods using miniaturized procedures are additionally beneficial because they tend to be generally less expensive, faster, and less labor intensive than conventional methods and, furthermore, they reduce analyst exposure to hazardous materials. Mass spectrometry (MS), interfaced with high‐resolution gas chromatography (GC) and high‐performance liquid chromatography (HPLC), has become the detection and quantification method of choice for herbicide analysis with many recent improvements due to advances in ionization techniques and analyzer power. These advancements have resulted in higher levels of accuracy, sensitivity, and selectivity, as well as higher sample throughput. Immunoassay (IA) techniques have also emerged as important tools in the detection of herbicides in biota.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".