Integrated Characterization of Toxicity Distribution of Selected Heavy Metals in Stream Sediments Around Itakpe Iron Ore Mines, North Central Nigeria
Bibliographic record
Abstract
The results of the analysis were subjected to five (5) indices: contamination factor (CF); Ecological risk factor (Eri); Enrichment factor (EF); index of geo-accumulation (Igeo) and anthropogenic factor (AF). The CF results showed that the River PomPom dry and wet season sediments were collected in February and July respectively from 0cm to 10cm depth. Stainless steel spoon was used to collect the samples. The sediment samples were packaged, labeled and refrigerated for onward transportation to the laboratory. All samples were oven-dried (50oC) for about 24 hours, sieved through -80 mesh. About 1.0g of each sample was digested, filtered and the filtrates were ready for heavy metal analysis using Atomic sediments were most contaminated with Fe in both seasons, least contaminated with Cd at dry season and Zn in wet season. The Eri factor revealed pollution hazard level of middle during wet season and tiny in dry season. Pb and Ni showed strong and middle hazard level during wet season and Cu revealed middle level during dry season. The enrichment factor indicated extremely enriched for Fe in all locations in both seasons while most heavy metals showed depletion to minimal enrichment. The Igeo for both seasons showed that Fe was moderately to highly polluted in all locations. During dry season, other heavy metals showed moderately to unpolluted while in wet season Cd, Ni and Pb indicated moderately to very highly polluted. In both seasons, the % AF was very high for Fe, Cu, Pb. The AF% was very high for Ni and Cd in wet season. All sites in both seasons have experienced deterioration but more in the wet season. From these indices, the stream sediments have been significantly contaminated with Fe, Ni, Pb and Ni in most locations in both seasons.
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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) | 0.000 | 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".