Assessing Seasonal Variability, Trend, Distribution and Degree of Heavy Metal Contamination in Urban Lake Sediment, Malaysia
Bibliographic record
Abstract
This study investigated whether there was an effective discriminant function that substantially depicted two-group seasonal variation and predicted metals’ posterior seasonal variability group membership. It also sought to quantify the degree of sediment contamination by both single and multiple metals. The analytical method involved discriminant analysis for the derivation of the discriminant function and adoption of single and integrated contamination indices for the determination of the degree of sediment contamination. The sediment samples were analysed using inductively coupled plasma mass spectrometry. The results from multivariate Wilks’ Lambda, canonical correlations and functions at group centroids provided clear evidence of significant discriminant functions that signified substantial dry and wet seasonal variations. In terms of relative contribution, Al, Fe, Pb, Na and Ni had the highest impact on the discriminant function. With 76% predictive accuracy, Al, Fe, Pb, Na were classified under the greater probability of dry season variability, while Ni was predicted to have the posterior probability of greater wet season variability. Based on the contamination factors, the level of sediment contamination by Pb and Na ranged from heavy to very heavy but Al and Fe showed moderate contamination. Based on the modified degree of contamination, the overall degree of sediment contamination by all the heavy metals analysed in the study ranged from moderate to heavy degrees of contamination. An assessment with the pollution load index, using trace metals (Pb, Mn, Cu, Ni, Cr), showed that the overall status of the degree of contamination was greater than the baseline level, indicating that the sediments were typically polluted and consequently in a progressive state of deterioration caused by the metals. In general, the degree of contamination was greater in the dry season and in the northern part of the lake.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".