Geochemical and chemometric analysis of soils from a data scarce river catchment in West Africa
Bibliographic record
Abstract
Abstract Metal levels beyond stipulated thresholds are a considerable concern for environmental pollution regulators and public health administrators around the globe. Data is, however, lacking in most regions especially developing countries for practical policy decision making and management. In this study, we obtain 49 high-resolution soil cores from three vertical profiles in the Densu River Basin of Ghana and measured the concentrations of major and trace metals (Ca, K, Fe, Ti, Cr, Cu, V, Ni, and Zn). The aim was to examine and provide data on metal levels to serve as baseline information on mobilization studies for waste management. Geochemical methods for estimation of metal enrichment and accumulation were employed to determine enrichment and pollution, sources, and mobilization of the metals. Hierichical cluster and principal components analyses were used to examine metal associations and the effects soil physicochemical properties on the metals. The results show spatial variations in metal concentrations within and between individual soil profiles and are attributed to variability in soil formation processes and the locations where samples were collected, respectively. Moderate to high enrichment factors (EF) and geo-accumulation (Igeo) indices were observed for Vanadium (V) and Chromium (Cr) in all soil profiles indicating some level of anthropogenic interference leading to pollution possibly from vehicular and agricultural inputs. The Pourbaix diagrams, however, show that the Cr and V abundances may be natural. Our analysis also show that most of the metals investigated are of natural (i.e., geologic) origin but further investigations are recommended. The combination of field observations and established methods such as geochemical and statistical analyses have aided in extracting beneficial information from the small sample size.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 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".