Effect of Phytoremediation on PAHs Levels of Agricultural Soil around Mechanic Village Wukari, Nigeria
Bibliographic record
Abstract
This study evaluates the PAHs composition in agricultural soils around Mechanic village, Wukari using standard procedures by means of GC-MS. The possible source of the PAHs in the soil was deduced, using the diagnostic ratio analysis for PAH origin. Risk assessment was based on the incremental life cancer risk of PAHs proposed by Provisional Guidance for Quantitative Risk Assessment of the United States Environmental Protection Agency. Effect of phyto-remediation using Zea mays inter-planted with Striga hermonthica (SMV-MS), Zea mays alone (SMV-M), Zea mays inter-planted with Striga hermonthica alongside the application of fertilizer (SMV-MSF) and Zea mays alone alongside fertilizer application (SMV-MF). The result reveals that the PAHs composition based on ring prevalence in agricultural soils around Mechanic village Wukari was in the order Σ5 >Σ6 >Σ4 >Σ3 >Σ2 ring. Dibenz [a,h] anthracene 4.52 μg/kg (39.09%) has the highest percentage abundance but was less than the 100 μg/kg and 700 μg/kg Canadian soil quality guideline for agricultural and commercial layout while acenaphthene 0.111 μg/kg (0.90%) was the least abundant. The source of the PAHs in the soil was basically pyrogenic based on the diagnostic ratio analysis while phytoremediation of the soil using Zea mays inter-planted with Striga hermonthica significantly reduce the PAHs content of the soil.
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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 | 0.000 | 0.000 |
| 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".