COMPARISON OF BRACKET WATER DESALINATION TESTS IN THE FORM OF POWDER, GRANULES AND GREEN ALGAE HYDROGEL
Bibliographic record
Abstract
This research was motivated by the problem of lack of clean water around the coast of Rainbow Beach in Karawang because the water quality is poor and still brackish. Untreated brackish water poses a risk to human health if drunk for a long time and can trigger skin diseases if used for bathing. The well water taken is located in the Pelangi Coastal Area, Pedes Karawang. This research aims to test the ability of brackish water desalination using powder, granules, and three variations of green algae hydrogel. This research uses a quasi-experimental method by comparing it with zeolite as a standard for brackish water desalination. The research results show that the resulting hydrogel preparation has a solid, brittle shape, a dark green color, and a distinctive odor of green algae. The best viscosity value is in the H4 formula. All green algae hydrogel formulas have good pH values, while the best swelling ratio value is in the H2 formula, and the best gel fraction value is in the H4 formula. From the results of the research that has been carried out, it can be concluded that the hydrogel, granule, and green algae powder preparations can desalinate brackish water based on the results of pH, temperature, salinity, sodium ion, and magnesium ion tests compared with zeolite, where the 6 g hydrogel preparation shows the best desalination capability of brackish water at the three well sample points
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".