Appraisal of Heavy Metal Contents in Commercial Inorganic Fertilizers Blended and Marketed in Nigeria
Bibliographic record
Abstract
Human food chain toxicity has been shown to be influenced by application of inorganic fertilizers. Three samples of commercial inorganic fertilizers marketed in Nigeria were analysed for their heavy metal contents. The fertilizer samples used were N15P15K15 (CF1), N20P10K10 (CF2) and N27P13K13 (CF3). Molybdenum (Mo) levels were the highest and Vanadium (V), Arsenic (As), Mercury (Hg) and Silver (Ag) were not detected by our analytical method. The concentrations of Mo in the CF1 (2, 570.0 mg/kg), CF2 (2,3000.0 mg/kg) and CF3 (1,340.0 mg/kg), were above the maximum acceptable concentration (20mg/kg) while CF1 (0.129 mg/ha/yr) and CF2 (0.115 mg/ha/yr) were above the maximum annual metal addition to soil (0.079 mg/ha/yr), as established by Canadian standard for fertilizers. Only CF1 (5.805 kg/ha) was above long-term cumulative assessment of metal addition to soil (3.57 kg/ha). Other heavy metals detected were Magnesium(Mg), Iron(Fe), Nickel(Ni), Zinc(Zn), Cadmium(Cd), Manganese(Mn), Cupper(Cu), Cobalt(Co), Lead(Pb), Chromium(Cr) and Boron(B). Cd and Pb mean concentrations of the fertilizer samples examined ranged from 2.84 to 11.32 mg/kg and 7.43 to 9.02 mg/kg respectively. Cd followed significantly (p<0.05) with phosphate percentage by weight of the inorganic fertilizers. Though the concentrations of these non-nutrient/toxic metals were below the recommended levels, it is important to encourage manufacturers to indicate their concentrations on the fertilizer labels in view of their health implications.
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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 | 0.000 | 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) | 0.001 | 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".