Determination of multi pesticide residues in leaf and needle samples using a modified QuEChERS approach and gas chromatography-tandem mass spectrometry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In order to gain a better insight into pesticide and pollutant exposure in forests, a rapid and sensitive gas chromatography-tandem mass spectrometry (GC-MS/MS) method for the determination of 208 pesticide residues in leaves and needles has been established. The modified QuEChERS (quick, easy, cheap, effective, rugged and safe) approach uses 2 g of homogenized sample, acetonitrile and water as extraction agents, combined with citrate buffer for the following salting out step. The limits of quantification (LOQs) were determined to 0.0025-0.05 mg kg-1, respectively. Calibration curves showed a linear range between the respective LOQ and 1.0 mg kg-1 with coefficients of determination (R2) ≥ 0.99 for all analyzed pesticides. The recovery rates ranged from 69.7% to 92.0% with a relative standard deviation below 20%. The analysis of beech leaves, spruce and pine needles (each n = 3) provided a proof of concept for the developed methodology and revealed the presence of six pesticide residues (boscalid, epoxiconazole, fenpropimorph, lindane, terbuthylazine, terbuthylazine-desethyl). The results underline the strong need for systematic surveillance of the uncontrollable exposure of pesticides to nature.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it