A comparison of pesticide residues in soils from two highly technified agricultural valleys in northwestern Mexico
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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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 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".