The Dynamics and Water Quality Status of a Tropical Coastal Lake in Anak Laut Lake, Singkil Indonesia
Bibliographic record
Abstract
Anak Laut Lake a tropical coastal lake in Singkil, Aceh Province, is directly connected to the Indian Ocean on the northwestern part of Sumatra Island. The existence of oil palm plantations in the northern part of Lake Anak Laut has the potential to pollute the lake waters. Therefore, this research aims to determine the dynamics of water quality which is influenced by the tidal cycle and the level of pollution of Lake Anak Laut. This research was conducted on March and April 2023. Water sampling was conducted in four (4) times, following the tidal cycle, namely new month, first quarter, full moon, and last quarter. The sampling was also carried out at high and low tides. The method for determining water pollution is the Canadian Council of Ministers of the Environment (CCME) and Pollution Index Method (PI). The results of the research show that the current and water level of Anak Laut Lake varies according to the tidal cycle, so the water quality fluctuates greatly following the tidal cycle. The concentration of organic matter and nutrients as well as Total Coliform showed higher in the last quarter phase where the water entering the lake was very low. The high level of organic matter is caused by 'waste' from palm oil plantation activities located north of Anak Laut Lake. The water quality status of Anak Laut Lake is categorized as marginal-poor based on CCME (score 42-66) and moderate based on PI (score 5-8). In general, water quality conditions are "better" at high tide (new month and full moon) than at low tide (first quarter and last quarter).
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".