Pollution status of Aneuk Laot lake Sabang based on pollution index and saprobic index
Bibliographic record
Abstract
Pollution that occurs in lake waters needs special attention from various parties in the management of the lake in the future. The activities occurring around the lake result in an increased inflow of pollution into the lake. The aim of this study is to assess the pollution condition of Lake Aneuk Laot in Sabang by utilizing indicators such as the pollution index, CCME WQI (Canadian Council of Ministers of the Environment Water Quality Index), and saprobic index. The investigation took place in both September 2019 and June 2021, employing the stratified random sampling method with four designated observation stations for the sampling process. Parameter measurements analyzed in the pollution index include temperature, depth, current, TDS, TSS, BOD, COD, DO, phosphate, nitrate, ammonia, sulfide, iron, lead, oil and fat, detergent, pH, e-coliform, and parameters used in the saprobic index include phytoplankton data. Based on the analysis of the Pollution Index and CCME WQI it is determined that the pollution status of Lake Aneuk Laot is heavily polluted for Class I, moderately polluted for Classes 1 and 2, and falls under the good category for Class 4. The saprobic index results show the beta-mesosaprobic category with a result of 2.3 (moderately loaded).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".