Spatial Distribution and Bioavailability of Some Essential Trace Elements in Southern Ondo State Nigeria
Bibliographic record
Abstract
The total elemental content of soil though may give abundance of element concentration but have been found not to be suitable for prediction of environmental bioavailability and toxicity by scientific community. Surface (0-30cm) and subsurface (60-90cm) soil profile in the Southern Ondo State Nigeria were investigated for spatial distribution, bioavailability and mobility of some essential trace elements (Cu, Fe, Mn, Zn). Their spatial distribution were very similar in both surface and subsurface soil environment indicating that similar geochemical factors may be responsible for their distribution. The North was composed of basement complex while the South was largely undifferentiated sedimentary rock. Higher concentrations of Cu, Fe, Mn and Zn were recorded in the North through the centre of study area to lower concentrations in the South. The spatial concentration of the trace elements may have been influenced by the nature of underlying bedrock type. Cu was potentially bioavailable in both surface and subsurface soil environment considering the fact that >50% of its total concentration were in the nonresidual fraction. Other trace elements were not bioavailable because >60% of their total concentrations were found in residual fractions. The relative risk assessment code of Cu (surface; subsurface) indicated progressive risk (MoF1, MoF2, MoF3) from low (2-10; 1-6) through medium (12-30; 10-21) to high risk (25-40; 21-35) in both surface and subsurface soil environment while Zn (surface) shows similar trend (1-5; 11-21; 22-35) only in the surface soil environment. Other elements show some level of risk to no risk. There is likelihood that Cu and/or Mn may be associated with anthropogenic sources.
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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".