Exploration of Indigenous Fungi: Identification of Fungal Isolates from Industrial Waste Disposal Rivers
Bibliographic record
Abstract
This research focuses on exploring indigenous fungi from industrial waste discharged into rivers. Sampling was conducted in the Ciliwung and Krukut rivers at several points in the waste disposal environment. The purpose of this study was to identify the presence of fungi that have the potential to degrade industrial waste components from the waste-polluted climate itself. The method includes isolating fungi by dilution from sediment and water samples, which are grown using potato dextrose agar (PDA) media. After the isolation, characterisation was done to identify the fungus types found. The results of this study showed the presence of 5 fungal isolates that have great potential in degrading industrial waste. These findings suggest that local fungi from Jakarta landfills could potentially be used for bioremediation as a solution to reduce the impact of hazardous waste on the environment. Waste management using fungi supports the achievement of SDG 6 (clean water and sanitation) by improving water quality through the degradation of harmful compounds, SDG 12 (responsible consumption and production) by providing sustainable waste management methods, and SDG 14 (marine ecosystems) by prevent river pollutants from reaching the sea to maintain the health of the aquatic ecosystem.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".