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Record W4392598150 · doi:10.5194/egusphere-egu24-3195

Could amending different organic wastes with Pulp and Paper Mill Sludge (PPMS) thrive the production performance of Eisenia fetida? 

2024· preprint· en· W4392598150 on OpenAlexaffabout
Dasinaa Subramaniam, Mano Krishnapillai, Lakshman Galagedara

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicComposting and Vermicomposting Techniques
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsEisenia fetidaPaper millPulp (tooth)Pulp and paper industryWaste managementEnvironmental scienceChemistryBiotechnologyEnvironmental engineeringAgronomyBiologyEngineeringMedicineDentistryEarthworm

Abstract

fetched live from OpenAlex

Large quantity (150 Mg/day) of Pulp and Paper Mill Sludge (PPMS) is being generated in Corner Brook Pulp and Paper Limited (CBPPL), Newfoundland, Canada. Since PPMS contains high level of organic matter-OM (80-85%) and moisture content-MC (50-60%), it may be considered for recycling as vermicompost and earthworm as animal feed. An initial attempt on vermicomposting PPMS using Eisenia fetida had a long processing time up to 80-90 days. Therefore, the current study was designed to shorten the processing time by amending PPMS with different organic wastes and monitoring production performance of Eisenia fetida.Organic wastes - poultry bedding material (SP), vegetable peels (SV), soil (SS), fresh cow manure (SC-L) and composted cow manure (SC-C) were amended separately with PPMS in 2:1 ratio as the treatments while PPMS alone was the control (S). About 14.1-14.5 g of earthworms/ 3.6 kg of substrate (an average stocking density of 4 g/kg) were introduced in all the treatments which were triplicated in completely randomized design. Changes in vermicompost parameters such as pH, electrical conductivity (EC), OM, MC, etc. were monitored weekly and the population dynamics of earthworms were studied, bi-weekly. The MC was maintained at about 75-80% in all the treatments. Results showed that amending organic wastes with PPMS had a significant (p< 0.0001) influence on the quality and quantity of the final vermicompost produced. The total quantity of vermicompost produced was higher (73.8%) in SC-L followed by SV (70.4%), SC-C (69.8%), SS (67.3%), S (64.3%) and SP (8.8%) in 45 -50 days. pH decreased in all the treatments except in control until 30 days and increased afterward to reach the range between 6.1 and 7.3. EC in all the treatments S, SV, SS and SC-L (except SP and SC-C) reduced from the initial value of 2.90, 2.66, 1.23 and 3.18 to 2.58, 2.56, 2.69 and 2.69 mS/cm, respectively. Simultaneously, OM content showed a decline in all the treatments, while the reduction rate was higher in SS (4.71%) > SV (4.02%) > SP (3.94%) > SC-L (2.91%) > SC-C (2.67%) > S (1.81%). Total biomass gain of Eisenia fetida was 56.1%, 40.4%, 22.3% and 13.4% in SC-L, SC-C, SV and SS, respectively in day 45. Conversely, a reduction in earthworm biomass of 55.8% and 60.1% was observed for S and SP, respectively. The average biomass growth rate was higher in SC-L (1.73 g/day) followed by SV (1.35 g/day) and SS (1.22 g/day). As a whole SC-L, SC-C, SV and SS had no significant difference (p> 0.05) among them in both total biomass gain and growth rate while those treatments had the significant difference with S and SP (P< 0.0001). Therefore, we conclude that the PPMS can be an excellent substrate to reuse in vermicomposting. However, incorporating various organic manures and wastes could enhance the vermicomposting rate and significantly reduce the processing time. Further improvements can be achieved by adjusting factors such as the types and ratio of organic wastes, and the number of earthworms involved.Keywords: Eisenia fetida, PPMS, cow manure, vegetable peels, poultry bedding, soil

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

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 armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Bench or experimentalhigh
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Bench or experimentalmedium
models agreeAgreement compares identical category sets and study designs across arms.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.298
Threshold uncertainty score0.376

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.025
GPT teacher head0.229
Teacher spread0.205 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes2
Has abstractyes

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