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Inhibitory Effects of Organic Acids on Bacteria Growth During Food Waste Composting

2010· article· en· W2060668681 on OpenAlexaff
Hui Yu, Guohe Huang, Xiaodong Zhang, Yu Li

Bibliographic record

VenueCompost Science & Utilization · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicComposting and Vermicomposting Techniques
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsBacteriaFood scienceMicroorganismChemistryFood wasteDecompositionBiodegradable wasteThermophilePopulationOrganic acidButyric acidAmino acidBiologyBiochemistryOrganic chemistryEcology

Abstract

fetched live from OpenAlex

During the process of food waste composting, organic acids are generated from microbial breakdown of easily degradable substrates. These organic acids could inhibit microbial activities and sequentially reduce the decomposition efficiency. Considerable relationships among pH, organic-acid concentration and microbial activity were observed in this study. In order to systematically investigate inhibitory effects of four organic acids on composting bacteria, multivariate experimental designs were implemented in day 5 and day 9 during the initial stage of composting. Butyric and propionic acids had significant inhibitory effects on the growth of thermophilic bacteria on day 5. The inhibitory effects of organic acids on composting bacteria became significantly milder on day 9. The effects of different pH control amendments to mitigate microbial inhibition were also preliminarily examined. The treatment of NaAc resulted in the largest population of thermophilic bacteria.

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)0.000
metaresearch head score (Gemma)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.017
GPT teacher head0.228
Teacher spread0.212 · 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
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

Citations17
Published2010
Admission routes1
Has abstractyes

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