METODA PENGHILANGAN LOGAM MERKURI DI DALAM AIR LIMBAH INDUSTRI
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Industry is a potential source of water pollution, it produces pollutants that are extremely harmful to people and the environment. Many industrial facilities use freshwater to carry away waste from the plant and into rivers, lakes and oceans. Inorganic industrial wastes are more difficult to control and potentially more hazardous Industries discharge a variety of toxic compounds and heavy metals. The most pollutans heavy metals are Lead, Cadmium, Copper, Chromium, Selenium, Mercury, Nickel, Zinc, Arsen and Chromium. Heavy metals are dangerous because they tend to bioaccumulate. Mercury for example, causes damages to the brain and the central nervous system, causes psychological changes and makes development changes in young children. Normally Mercury is a toxic substance which has no known function in human biochemistry. There are several methods to eliminate or remove mercury in water such as chemical oxidation process, ion exchange process, adsorption process, an electrochemical process, reverse osmosis process and other alternative methods likes biosorption. Each method has strengths and weaknesses, therefore to choose the method of removing of mercury in wastewater depending on pollutants conditions such as concentrations of mercury in wastewater, types of mercury, mercury concentrations in treated water, land availability, flow rate of wastewater will be processed and other parameters. This paper discusses several methods of removal of mercury heavy metals in industrial wastewater such as chemical precipitation and oxidation processes, adsorption and ion exchange process. Keywords : water pollution, heavy metals, mercury, industrial wastewater, removal methods.
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.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it