DECOMPOSER ACTIVITIES OF EUDRILUS EUGENIAE, EISENIA FETIDA, AND LUMBRICUS RUBELLUS: QUALITY OF MUSHROOM WASTE SUBSTRATE VERMICOMPOST
Bibliographic record
Abstract
This study aimed to analyze the vermicompost quality of mushroom substrate waste (without other substrates) produced by Lumbricus rubellus, Eisenia fetida, and Eudrilus eugeniae worms. The chemical quality of vermicompost was analyzed using the Kjeldahl and 25% HCl extraction and measured by spectrophotometer and flame photometer. Meanwhile, the biological quality was analyzed using the Total Plate Count, Nelson-Somogyi, and Most Probability Number (MPN) methods. Vermicompost's chemical and biological quality was compared with the compost quality standards in Indonesia and abroad. The results showed that the levels of N+P2O5+K2O, micronutrients (Fe, Mn, Zn), and heavy metals (Pb and Cd) in vermicompost were 5.9%, 0.95-27.5 ppm, and < 0.25 ppm, respectively. The levels of N+P2O5+K2O are by the quality standards of compost in Indonesia, Tanzania, the Philippines, and Thailand; the micronutrients (Fe, Mn, Zn) are by the means in Indonesia. The heavy metals (Pb and Cd) levels meet the applicable standards in Indonesia, England, Belgium, Germany, Italy, Canada, and the USA. The contents of E. coli, N-fixing microbes, and phosphate solubilizing microbes (PSM) in vermicompost were < 26.5 MPN g-1, 1.00' 105 - 31.00' 105 cfu g-1, and 0.40' 105 - 39.00' 105 cfu g-1, following the applicable standards in Indonesia and Europe (E. coli with a maximum of 1000 MPN g-1 and the number of N-fixing microbes and PSM of at least 1.00' 105 cfu g-1).
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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".