Multivariate statistical technique in the assessment of coastal water quality of Oman
Bibliographic record
Abstract
Coastal water plays a significant role in the growth and productivity of marine organisms; and is the source of many important economic activities including fishery, coastal recreation and socio-economic development. Coastal water quality is highly influenced by the physical, chemical and biological processes in the ocean. The present study assessed 17 physico-chemical water quality parameters along the Omani coastline of Capital Muscat at Mina Al Fahal, Al Ghubrah, Barka and Quriyat. The results showed that the level of dissolved oxygen (O2) was lower than 5 mg/l at the Mina Al Fahal (2.24 mg/l) and Quriyat stations (3.68 mg/l), signifying a threat to coastal species. Relatively high total dissolved solids (>40 000 mg/l), total coliform (>500 most probable number (MPN)/100 ml) and Escherichia coli (>35 MPN/100 ml) at the Al Ghubrah and Barka stations indicated contamination from brine disposal, local run-offs, decomposition of organic waste and domestic effluent discharges. The higher phosphate–phosphorus level of 0.14 mg/l at Quriyat suggested pollution from local fish waste and sediment decomposition. The analysed parameters were further evaluated using multivariate statistical techniques such as cluster analysis (CA) and principal component analysis/factor analysis (PCA/FA). CA provided five clusters after grouping the parameters based on similarity. PCA/FA identified four main loading factors, with a total variance of 74.2%. The analysis showed contamination from desalination plants, domestic waste, local discharge and natural fish wastes and weathering of seabed rocks and sediments as the main contributors to the elevated concentrations affecting the water quality in the study locations.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".