Bibliographic record
Abstract
Ciliwung River which has upstream in Bogor, while downstream in Jakarta presence has very important role for the region in its path. Positive role of the river for the public interest in this area include the need of clean water sources, irrigation / agriculture, industry and others. Conversely Ciliwung River is also a source of flood disaster in Jakarta. Will consider its use, the risk of disaster resources and environmental burdens are received by the waters and the water catchment area is very large, then the existence of river quality should be monitored both the water catchment conditions or water quality. For future purposes, it would require a management with specific deadlines (5 th, 10 th, 15 th) and targets based on the quality of the water quality standards (BMA) class III, II and I. The results of the current monitoring parameters BOD, COD, fecal Coli, Coliform has a value above the threshold of water quality standards specified. While DO in the downstream segment has a value below the water quality standards. Other parameters such as pH, TSS and nitrogen have a greater trend downstream, but the value of the water quality is still under water quality standards that are targeted. Keywords: Water quality, Water Quality standards, Ciliwung river.
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 | 0.000 | 0.001 |
| 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) | 0.004 | 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".