Optimization of Stocking Density of Eisenia fetida in Bioconversion Process of Pulp and Paper Mill Sludge (PPMS) and Its Population Dynamics
Bibliographic record
Abstract
The pulp and paper mill industry in Corner Brook, Newfoundland and Labrador, Canada produces 150 Mg/day of pulp and paper mill sludge (PPMS) with a high moisture content (70–80%). Instead of the current practice of burning, PPMS may be repurposed as vermicompost. However, the optimum population density of Eisenia fetida to maximize the bioconversion process and its influence on population dynamics are largely unknown. The current study aimed at determining how stocking densities affect the vermicomposting time, vermicompost quality, and population dynamics of E. fetida. By using three stocking densities—1.13 g (TL), 2.04 g (TM), and 3.01 g (TH)—the E. fetida per kg of PPMS and the quality of the vermicompost were determined by chemical analysis followed by a germination test and bioassay on Raphanus sativus seedlings. The total vermicompost produced in TL (66.3%) and TM (68.8%) made the bioconversion process quicker in 60 days. The addition of more E. fetida (TH) delayed the process of vermicomposting by up to 90 days (p < 0.003). Overall, 0.87 (TM) E. fetida/L of PPMS was found to be the optimum population density for obtaining the best quantity and quality of vermicompost. The highest earthworm biomass was harvested in TL, followed by TM and TH, as 3.2, 1.3, and 0.9-fold, respectively, compared to the introduced biomass in 60 days (p < 0.008). The mean growth rate (2.6 mg/worm/day), biomass gain (3.49 mg/g), and reproduction rate (3.9 cocoon/worm) were also significantly higher (p < 0.023) in TL compared to TM and TH in 60 days. Therefore, the present study shows the importance of using an optimum stocking density to maximize the bioconversion process in PPMS, especially in cooler regions.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
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 | 0.000 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".