Herbicide Residues in Biota, Analysis of
Bibliographic record
Abstract
Abstract Current extraction, derivatization and clean‐up techniques, and instrumental methods are reviewed for the analysis of herbicide residues in biota. Sampling procedures are shown to be an integral part of the methodology. Herbicide analysis is seldom based on analyte‐specific methods but is usually integrated into multiresidue methods (MRMs). Current methods generally utilize relatively small sample sizes and miniaturized apparatus to take advantage of advances in instrumental performance and detection of analytes. These methods reduce the amount of solvent used for sample preparation and help to minimize waste generation. As well as using less solvent, the resulting miniaturized methods tend to be generally cheaper, 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), high‐performance liquid chromatography (HPLC) and capillary electrophoresis (CE), has become the detection method of choice for herbicide analysis. MS is well suited to the confirmation of target analytes and the tentative identification of unknown analytes. HPLC–mass spectrometers are becoming more widely available and less expensive. CE is a relatively new separation technique providing many advantages over traditional gas and liquid chromatography, including shorter analysis times and smaller injection volumes. There have been advances in the development of immunoassays primarily for the rapid screening for herbicide residues. These methods, once optimized, can facilitate high sample throughput at relatively low cost compared to conventional approaches. To date however, development has been limited to aqueous systems and little immunoassay work has been done for the direct determination 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 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".