Water Quality Monitoring Using Biological Indicators in Cameron Highlands Malaysia
Bibliographic record
Abstract
Macroinvertebrates are easily available, identified and have been used as bio-monitoring agent successfully. It is useful in detecting transient and longtime pollution to our aquatic system. The aim of this study was to determine the relationship between river water quality and the macroinvertebrates organism in the stream. Pauh River in Cameron Highlands, Malaysia has been chosen for this study. A total of six monitoring stations along Pauh River were setup in this study. In-situ field investigation and water sampling was conducted. Malaysian’s Water Quality Index (WQI) for the 6 sampling stations are calculated and compared with the macroinvertebrates sample. The pattern of distribution and abundance of different macroinvertebrates which correspond to polluted and non-polluted parts of each river studied suggested macroinvertebrates could be used as potential indicators for bio-monitoring in Malaysia.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".