Gas Extraction from Sludge as Acquired from Oxidation Ponds of Community Wastewater and Cassava-Factory Wastewater Treatment through Nature-by-Nature Processes
Bibliographic record
Abstract
The study was aimed on determining the gas volume from sludge of oxidation ponds for community wastewater treatment and UASB tank of cassava factory for wastewater treatment in which the organic matters of both units were digested through the nature-by-nature process. The amounts of oven- dry weight sludge about 200 g were collected in the light brow glass bottle with 2.5-l capacity. The fermentation of organic matters in sludge is the process to produce gases and being transferred to store in chamber by fluid displacement. The gases from sludge of oxidation pond was occurred on the second day and the maximum on the sixth day with the rate of 70 ml/d and average of 36.02 ml/d (total 360.23 ml for 10 days) while cassava factory sludge found the maximum volume on the first day with the rate of 142.6 ml/d and average of 72.2 ml/d (total 649.97 ml for 9 days). In other words, the oxidation pond sludge can produce gas 1.8 ml/g (oven dry weight) while the cassava factory sludge found gas 3.25 ml/g (oven dry weight). Research results found gases of oxidation pond sludge on the range of methane concentration between 545,686 – 9,560,606 ppm, hydrogen sulfide 55.94 to 360.27 ppm, and ammonia ND to 36.22 ppm, while the cassava factory sludge found methane gas concentration between 729,404 to 9,900,837 ppm, hydrogen sulfide 5,894 to 68,050 ppm, and ammonia ND to 44.15 ppm.
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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) | 0.000 | 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 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".