Distribution and ecological risks of toxic metals in the topsoils in the Kumasi metropolis, Ghana
Bibliographic record
Abstract
The distribution, and ecological risks due to toxic metals in topsoils of an urban commercial centre of Kumasi metropolis were determined. Concentrations of heavy metals in the samples were initially screened using field portable X-ray fluorescence spectrometer prior to confirmation on an inductively coupled plasma-mass spectrometer. The results from the two instruments correlated well with high linearity (R2 > 0.70). Concentrations of metals were in the order: Fe (45246.15 mg/kg) > Mn (347.86 mg/kg) > Zn (176.40 mg/kg) > V (173.10 mg/kg) > Cr (125.12 mg/kg) > Pb (53.47 mg/kg) > Ni (43.16 mg/kg) > Cu (39.85 mg/kg) > As (4.59 mg/kg) > Sn (3.69 mg/kg) > Cd (0.46 mg/kg) > Hg (0.28 mg/kg). Multivariate and geostatistical analyses exploring various hazard indices such as geo-accumulation, contamination, pollution load and ecological risks all suggest contamination of the topsoils with toxic metals and that the contamination due to Pb, Cu, Ni, Cr and Zn is mainly derived from anthropogenic origins whereas, As, Hg, Mn, V, Sn and Cd contaminations are attributable mainly to geological and atmospheric depositions. Contamination factor (CF) suggests that the topsoils were considerably contaminated (CF 3–6) with Zn, moderately contaminated (CF 1–3) with Ni, Hg, V, Sn and Cd and least contaminated (CF < 1) with As, Pb, Cu, Cr and Mn. In general, the soils were found to be moderately contaminated (average contamination factor (1 ≤ CF < 3) with toxic metals and may pose both human and ecological risks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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".