Contamination of Roadside Soil and Bush Mint (Hyptis suaveolens) with Trace Metals along Major Roads of Abuja
Bibliographic record
Abstract
There has been a growing concern over environmental pollution by trace metals from automobile source. Abuja, like most urban cities has a high road traffic density. The present study investigates the levels of trace metals in roadside plant and soils along some major roads in Abuja. Thirty samples, consisting of equal number of plants and soils from Airport, Kubwa and Nyanya road were analyzed for Pb, Fe, Cu, Zn, and Cr levels using atomic absorption spectroscopy. The findings reveal trace metal contamination gradient, with the maximum levels closer to the road. Copper is prevalent in the study area with concentrations standing at 76.66 ± 12.02 µg g-1 and 300.00 ± 50.00 µg g-1 in the plant and soil respectively. There is a significant correlation in the concentration of the metals studied regardless of sample class. The average distribution of the metals in the samples decreased in the order Cu > Zn > Fe > Pb > Cr with the exception of Nyanya soil and the plant samples from Kubwa road. Evidence for Pb transfer from soil to Hyptis suaveolens was established and accumulation of Pb, Cu and Fe has reached alarming levels. Chromium traces were as low as 11.91 ± 1.38 µg g-1 in the plant but reached up to 39.68 ± 6.87 µg g-1 in the soil. Concentration of the metals investigated in the soil except for Cu, are within the safety limit recommended by FAO/WHO.
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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) | 0.001 | 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".