A comparison of pesticide residues in soils from two highly technified agricultural valleys in northwestern Mexico
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
A pesticide characterization is presented for two highly technified valleys in northwest Mexico: Culiacan Valley (CV) in Sinaloa and Yaqui Valley (YV) in Sonora. Approximately 250,000 kg of active ingredients are used every year, half of which are considered highly hazardous pesticides. Legacy pesticides are still present in the soils of these valleys. The aim of the present study was to identify and quantify a wide variety of pesticides in soils and correlate their concentrations with historical and current use. Agricultural soils from both valleys were sampled and analyzed using accelerated solvent extraction and subsequent quantification by gas chromatography with selective detectors. The most frequently detected pesticides (mean, µg g−1) in CV were organochlorines (0.1967), organophosphates (0.0928), synthetic pyrethroids (0.2565), organonitrogen (0.0552), and miscellaneous pesticides (0.1851). In YV, the most frequently detected pesticides were organochlorines (0.8607), organophosphates (0.0001), synthetic pyrethroids (0.0124), and miscellaneous pesticides (0.0009). The pesticides were more diverse in CV compared to those of YV, which was based on the types of crops produced. Both locations presented highly hazardous pesticides, including concentrations above the action levels established by the Canadian Soil Quality Guide. A follow-up risk assessment is recommended to assess potential effects.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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