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Record W2936650506 · doi:10.3390/molecules24081466

Comparison of Two Process Schemes Combining Hydrothermal Treatment and Acidogenic Fermentation of Source-Separated Organics

2019· article· en· W2936650506 on OpenAlexafffund
Long Lin, E. Hosseini Koupaie, Armineh Azizi, Amir Abbas Bazyar Lakeh, Bipro Ranjan Dhar, Hisham Hafez, Elsayed Elbeshbishy

Bibliographic record

VenueMolecules · 2019
Typearticle
Languageen
FieldEngineering
TopicAnaerobic Digestion and Biogas Production
Canadian institutionsGreenfield Research (Canada)Toronto Metropolitan UniversityUniversity of Alberta
FundersOntario Water Consortium
KeywordsAcidogenesisFermentationPropionateChemistryFirmicutesHydrolysisFood sciencePulp and paper industryAnaerobic digestionBiochemistryOrganic chemistryMethane

Abstract

fetched live from OpenAlex

This study compares the effects of pre- and post-hydrothermal treatment of source- separated organics (SSO) on solubilization of particulate organics and acidogenic fermentation for volatile fatty acids (VFAs) production. The overall COD solubilization and solids removal efficiencies from both schemes were comparable. However, the pre-hydrolysis of SSO followed by acidogenic fermentation resulted in a relatively higher VFA yield of 433 mg/g VSS, which was 18% higher than that of a process scheme with a post-hydrolysis of dewatered solids from the fermentation process. Regarding the composition of VFA, the dominance of acetate and butyrate was comparable in both process schemes, while propionate concentration considerably increased in the process with pre-hydrolysis of SSO. The microbial community results showed that the relative abundance of Firmicutes increased substantially in the fermentation of pretreated SSO, indicating that there might be different metabolic pathways for production of VFAs in fermentation process operated with pre-treated SSO. The possible reason might be that the abundance of soluble organic matters due to pre-hydrolysis might stimulate the growth of more kinetically efficient fermentative bacteria as indicated by the increase in Firmicutes percentage.

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 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 score0.036
Threshold uncertainty score0.275

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 science0.0000.000
Research integrity0.0000.000
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 head0.013
GPT teacher head0.283
Teacher spread0.270 · 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 teacher head, not a consensus.

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

Citations12
Published2019
Admission routes2
Has abstractyes

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