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Record W2003065171 · doi:10.1080/10934529.2013.744637

Effect of different buffer agents on in-vessel composting of food waste: Performance analysis and comparative study

2013· article· en· W2003065171 on OpenAlexafffund
Sheng Li, Guohe Huang, Chunjiang An, Hui Yu

Bibliographic record

VenueJournal of Environmental Science and Health Part A · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicComposting and Vermicomposting Techniques
Canadian institutionsUniversity of New BrunswickUniversity of Regina
FundersNatural Sciences and Engineering Research Council of CanadaNational Science Foundation
KeywordsFood wasteAmmoniaChemistryAmendmentThermophileFood scienceOxygenBiodegradable wasteWaste managementBiochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

This study investigated the performance and feasibility for application of different buffer agent combinations, including K2HPO4/MgSO4, KH2PO4/MgSO4 and NaAc, in composting of food waste. The variations of temperature, pH, O2 consumption, organic mass and ammonia release were monitored. The results showed that addition of all these three types of agents could prolong the thermophilic stage during composting. The amendments of KH2PO4/MgSO4 and NaAc could increase and decrease the final pH levels, respectively. Application of K2HPO4/MgSO4 and NaAc would lead to a peak daily oxygen uptake rate of 10.0 and 12.4 mg/(g·h) respectively, which were all higher than that with KH2PO4/MgSO4 amendment. Similarly, the reactors with K2HPO4/MgSO4 and NaAc were also associated with a higher cumulative oxygen uptake and total organic degradation rate. The amendment of NaAc resulted in a higher ammonia loss than the other two agents. More inorganic nitrogen contents were observed in the series with K2HPO4/MgSO4 and NaAc. It can be concluded that K2HPO4/MgSO4 additive showed the most favorable influence on composting performance. The results of this study will have important implications for developing appropriate treatment approach for food waste composting.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.158

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.052
GPT teacher head0.311
Teacher spread0.259 · 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 designObservational
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

Citations26
Published2013
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Environmental Science and Health Part ASame topicComposting and Vermicomposting TechniquesFrench-language works237,207