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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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 teacher head, 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".