Eisenia fetida as a bioengineering tool for enhancing the degradation of hydrocarbon contaminants found in Pulp and Paper Mill Sludge (PPMS)
Bibliographic record
Abstract
Environmental contamination by petroleum hydrocarbons originating from industrial organic waste can lead to bioaccumulation within ecosystems. The Corner Brook Pulp and Paper Limited produces approximately 150 Mg/day of pulp and paper mill sludge (PPMS) contaminated with heavy oil, thereby limiting its safe disposal. Therefore, this study aimed to determine how the stocking density of the earthworm Eisenia fetida influences the degradation of petroleum hydrocarbons in contaminated PPMS and, in turn, how hydrocarbon contamination affects earthworm population dynamics during vermicomposting. Three stocking densities of E. fetida (1.5low, 2.7medium, and 4high) per kg of PPMS were maintained in PPMS having an initial petroleum hydrocarbon content of 886 ±11 mg/kg. Overall, hydrocarbon degradation was highest in the medium-density (36.6%), followed by low (35.9%) and high (32.4%) densities. Among the hydrocarbon fractions, >C16–C21 showed the highest degradation in the low-density (67.2%), whereas >C21–C32 hydrocarbons were most effectively degraded in the medium-density (28.4%). The C6–C10 fraction remained unchanged in the low-density E. fetida but decreased by approximately 50% in the medium- and high-density. Higher initial stocking density also resulted in increased E. fetida mortality. These findings highlight the importance of selecting an appropriate initial stocking density of E. fetida for effective degradation of petroleum hydrocarbons during vermicomposting of PPMS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".