Regression models for the prediction of water quality in the stormwater of urban arid catchments
Bibliographic record
Abstract
Data from five residential urban arid catchments were used to develop regression equations for predicting mean concentrations of selected heavy metals in the stormwater runoff from duststorm and stormwater flow properties. The selected metals are copper (Cu), lead (Pb), nickel (Ni), zinc (Zn), and iron (Fe). The concentrations of the selected metals were predicted through two groups of equations. The first group of equations relates concentrations of suspended sediment with duststorm and stormwater parameters, whereas the second group relates the concentrations of the suspended sediment with those of heavy metals in the stormwater runoff. The results of the predictions encouraged recommendations on the use of the equations in the investigated catchments and identified the relative importance of the stormwater runoff and duststorms on the accumulation and transportation of heavy metals in the stormwater runoff. Based on the obtained results, recommendations concerning water quality control in arid areas are made.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".