Assessment of heavy metal (Lead, Nickel, Zinc) in palm oil plantation soil and its potential health risk at Pasir Salak, Perak / Syarifah Nur Nadiah Syed Jaafar
Bibliographic record
Abstract
According to Justice O. Odoi (2011), soil heavy metals have been a useful sign to the environmental quality. Soils are functioning in providing the basis for food and biomass production, controlling and regulating environmental interactions, providing valued habitats and sustaining biodiversity. Study location was selected at two oil palm plantation at Kg.Changkat Rambai, Pasir Salak. Chemical analysis was used for analysis of lead, nickel and zinc. A statistical analysis that is statistical package for the social science (SPSS) version 18 was used in this study. Concentration of Pb, Ni and Zn have been detected in all sampling points. But the concentration are varies for each heavy metal. In addition, there was significant difference between lead concentration in soil and sampling location (p<0.05). While there were no significant difference between both nickel and zinc concentration with sampling location (p>0.05). Only zinc concentration for both sampling point comply with the Contaminated Land Management and Control Guidelines while nickel concentration for both sampling point are exceed the guideline. All heavy metal concentration for both sampling location were comply with the Canadian Environmental Quality Guideline. Health risk assessment found out that there is no adverse health effect (HI<1) associated with the exposure of all heavy metal via dermal contact and inhalation for farmers.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".