Dataset on specific UV absorbances (SUVA254) at stretch components of Perak River basin
Bibliographic record
Abstract
Perak River basin is in Perak state of Peninsular Malaysia. In this research, the river stretch serves as water intake for domestic, agricultural and industrial purposes in Perak Tengah, Hilir Perak and Manjung regions. It is located in mixed use area whilst exposing the river to anthropogenic elements. The sampling locations were conducted at selected points of Perak River namely Tanjung Belanja Bridge (TBB), Water Treatment Plant Parit (WTPP), Parit Town discharge (PTD), Water Treatment Plant Senin (WTPS) and Water Treatment Plant Kepayang (WTPK). The existence of aromatic hydrocarbons in freshwater samples was pre-assessed via qualification analysis; specific ultraviolet absorbance (SUVA254) method at 254 nm of wavelength. The SUVA dataset were 48.38 L/mg-m (TBB), 50.54 L/mg-m (WTPP), 8.05 L/mg-m (PTD), 85.75 L/mg-m (WTPS) and 217.39 L/mg-m (WTPK). The SUVA254 values of fresh water at the river basin have exceeded the water quality standards value equivalent to 2.0 L/mg-m permitted by the Environmental Protection Agency of United States. The exceeding values were an indication of a large portion of aromatic compounds in the water. Qualification analyses evident the existence of water pollutants at treacherous concentrations for public health in freshwater samples of Perak River basin. Thus, this research has presented important findings towards further research and countermeasure for a better alternative of water treatment in Malaysia.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".