Risk assessment of organochlorine re pesticide recidue in Nigeria cannabis sativaL.
Bibliographic record
Abstract
Cannabis is a versatile crop with a wide range of agricultural and industrial uses. This study aimed to investigate potential health risks associated with organochlorine pesticide residues (OCPs) and heavy metals in different parts of Cannabis sativa L. plant. Gas Chromatography-Mass Spectrometry (GC-MS) and Atomic Absorption Spectroscopy (AAS) were employed for the identification and quantification of OCPs and heavy metals, respectively. The OCPs results showed uniform presence of heptachlor (0.74-0.75 mg/kg), aldrin (0.34-0.35 mg/kg), and endosulfandiol (1.31-1.35 mg/kg) across all plant components. Endrin ketone was significantly higher in leaves (2.755 mg/kg), and p,p'-DDE was detected only in seeds (0.45 mg/kg) and blend samples (0.45 mg/kg). Concentrations of heptachlor, aldrin, endrin ketone, and p,p'-DDE exceeded Health Canada's Maximum Residue Limits (MRLs). Conversely, endosulfandiol levels remained within regulatory thresholds, and trans-Chlordane, 4,7-methanoindene, and methoxychlor were below detection limits in all plant parts. The health risk assessment, evaluated using estimated daily intake (EDI), hazard index (HI), and target hazard quotient (THQ) metrics, revealed that while most pesticide residues posed minimal health risks (HI < 1). THQ values for all analyzed pesticides were below 1.0, indicating low risk of non-cancer health 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 | 0.000 |
| 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 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".