Baseline Monitoring of Elemental Contamination Levels in Soil Samples in Elebele Community, Bayelsa State, Nigeria
Bibliographic record
Abstract
This study evaluated the physico-chemical properties of the soil and water in Elebele Community in Ogbia Local Government Area of Bayelsa State, Nigeria. Standard sampling and analytical methods were employed. The predominant soils of the region are mainly sandy-loam and clayey-loamy. The soil physico-chemical properties were in good status as they were not toxic. Soil particle size distribution (sand silt and clay) was observed as follows: sand content ranged between 50.6%-86.2% with a mean of 64.5% at the surface soil while the subsurface soil ranged between 35%-80.2% with a mean of 60.2%. Silt on the other hand ranged between 7.8%-36% and a mean of 25.1% at the surface and ranged between 12.8-49.6% and mean of 25.4% at the subsurface while clay ranged between 3.4%-16% and mean of 9.4% at the surface and also ranged between 7%-16% and mean value of the soil. However, the soil physical properties were relativity good for sustainability. Also, the metals studied were detected in all the sites. Generally the concentrations of the metals were highest at the top soils. This is expected since the top soil is the point of contact. The metal levels in all the sites were significantly higher than the levels observed in the control sites. Sources of heavy metals in soils like inorganic fertilizers and pesticides need to be controlled.
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 | 0.001 | 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".