Use of Multivariate Statistical Analysis for Detecting Spatial and Seasonal Attributes of Surface Water Quality
Bibliographic record
Abstract
Use of multivariate statistical analysis for detecting spatial and seasonal attributes of surface water qualityThe existence of both point and non-point inputs of pollutants raises the cost of water body treatment due to their negative effect on watersheds.Additionally, categorizing the most significant surface water quality parameters (SWQPs) in both spatial and temporal domains is crucial.Thus, to classify the dominant SWQPs and accordingly identify both spatial and temporal aspects of surface water quality, multivariate statistical analysis (MSA), such as principal component analysis/factor analysis, cluster analysis, and discriminant analysis, was used.The obtained results demonstrated that turbidity, total suspended/dissolved solids, chemical oxygen demand, and biochemical oxygen demand are the dominant SWQPs, which contribute to spatial and temporal surface water quality status of the Saint John River, Canada.Moreover, a decrease in the dimensionality of surface water quality data was achieved.To conclude, the use of MSA can lead to effective savings and applicable exploitation of water resources.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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".