MétaCan
Menu
Back to cohort

Anaerobic co-digestion of mixed activated sewage sludge and fruit and vegetable waste on two-stage digester stability

2020· dataset· en· W4231412596 on OpenAlexaff
Herald Wilson Ambrose, Ligy Philip, G. K. Suraishkumar, Tushar Kanti Sen

Bibliographic record

VenueAuthorea · 2020
Typedataset
Languageen
FieldEngineering
TopicAnaerobic Digestion and Biogas Production
Canadian institutionsBentley (Canada)
Fundersnot available
KeywordsAnaerobic digestionMesophileSewage sludgeThermophileDigestion (alchemy)ChemistryPulp and paper industryMethaneFood scienceActivated sludgeAnaerobic exerciseWaste managementSewage treatmentBiologyChromatographyBiochemistryBacteriaOrganic chemistry

Abstract

fetched live from OpenAlex

Co-digestion of mixed waste activated sludge (MWAS) and fruit and vegetable waste (FVW) was studied in a two-stage (thermophilic followed by mesophilic) semi-continuous anaerobic digestion to evaluate anaerobic digester performance and stability. A mixing ratio of 75% MWAS and 25% FVW showed a 1.6-fold increase in overall methane yield and achieved 0.38 FOS/TAC ratio (volatile fatty acids to alkaline buffer capacity) compared to mixture of 50% MWAS and 50% FVW. Application of hybrid (MW-H2O2) pretreatment in the former mixing ratio increased sludge solubilisation by 33% and consequently enhanced overall methane yield by 2.17-fold. The treated digester showed increased process stability with a FOS/TAC ratio of 0.26 as a consequence of buffer capacity offered by released biopolymers during pre-treatment. The generation of superoxide radicals during digestion was studied and found to negatively correlate with sludge bioactivity. Two-stage digestion also minimizes the issue of high acidification due to co-digestion involving FVW.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.012
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.007

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 head0.023
GPT teacher head0.245
Teacher spread0.222 · 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

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

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

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

Citations4
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueAuthoreaSame topicAnaerobic Digestion and Biogas ProductionFrench-language works237,207