Assessment Of Level Of Polychlorinated Biphenyls (PCBs) In Soil And Edible Crops From River Ala Floodplain In Akure
Bibliographic record
Abstract
Abstract Flooding is one significant process that contributes to the movement of soil and sediments with the pollutants present in it along the river bank. With higher velocities of water, flooding can take contamination downstream. The soil, edible crops (rice and vegetables) samples were collected from Ala river floodplain in Ondo State and analyzed for their physico-chemical characteristics and level of Polychlorinated biphenyls present. The concentrations of nineteen (19) polychlorinated biphenyls (PCBs) were analyzed in soils collected at nine different sites along the floodplain and edible crops collected from six of the sites/ location using Gas Chromatography Mass Spectrometer (GC-MS) to determine the level of contamination of the floodplain and potential risks to the ecosystem and humans. The Ʃ-19 PCB concentrations in the floodplain soils varied between not detected (nd) -60 µg kg −1, n.d – 60 µg kg −1 in vegetables and not detected to 80 µg kg −1 in rice across different locations. Higher concentration values were recorded in rice which suggests plants uptake and bioaccumulation potentials.The concentration of di to tetra homologues are more predominant in the samples. The concentration is lower than the value of permissible limit 2mg kg-1 recommended by Canadian Soil Quality Guideline (CSQG) and United States Environmental Protection Agency (US EPA). Dioxin-like PCBs were not detected
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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 | 0.001 |
| 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) | 0.000 | 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".