Perhitungan Mutu Air Laut Di Perairan Sekitar Pembangkit Listrik Tenaga Uap
Bibliographic record
Abstract
One of the causes of the decline in sea water quality is due to industrial activities near sea waters, one of which is Steam Power Plants where the cooling system activity process of the generating equipment becomes a problem because it is thrown into the sea at high temperatures. Based on this, it is necessary to calculate the status of sea water quality in order to find out whether sea water quality has decreased due to the PLTU's activities. The aim of this research is to determine the quality profile of sea water and determine the impact of activities in the surrounding waters at several monitoring points around the waters of the Steam Power Plant. The way to determine or status method for water quality is with a pollution index based on Decree of the Minister of Environment Number 115 of 2003. Water quality can be assessed based on the classification of pollutant indicators, the pollutant index value around the Steam Power Plant in 2020 Quarter II is 7.86 at the point 6. Meanwhile, in 2021 and 2022 there will be a decline in every quarter and at all points, for determining quality status refers to Minister of the Environment Regulation No. 2003 No. l115, in 2020 Quarter II, the quality status of sea water around the waters of the Steam Power Plant was included in the moderately polluted category and in both 2021 and 2022 the quality status of sea water around the waters of the Steam Power Plant was included in the lightly polluted category.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.082 | 0.028 |
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".