Penentuan Status Mutu Air Sungai Wrati Pasuruan Jawa Timur dengan Indeks Kualitas Air
Bibliographic record
Abstract
The Wrati River is located in Pasuruan Regency. This river crosses three sub-districts and six sub-districts, with a total distance of 13.6 kilometers. The subdistrict through which this river passes has land use for household, agricultural, and industrial purposes. These activities hurt rivers, causing pollution and decreasing water quality. Determining water quality status is very important to understand the suitability of river water for various purposes. Understanding water quality can be fundamental information for managing and preventing river pollution. The pollutant index approach used in this research refers to the guidelines in the Decree of the Minister of the Environment Number 115 of 2003. The research results show that the water quality of the Wrati River from upstream to downstream is included in the lightly polluted category at each sampling point, with an index value of 4.94. The main factor causing light pollution in Wrati River water is the excessive phosphate content and other parameters that exceed the specified quality standards. Based on the findings of this research, waste management efforts need to be made to improve the quality of this river water.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".