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Record W2896685039 · doi:10.1371/journal.pone.0205112

Evaluation of compost, vegetable and food waste as amendments to improve the composting of NaOH/NaClO-contaminated poultry manure

2018· article· en· W2896685039 on OpenAlexaff
Yuting Liu, Wenxia Wang, Jianqiang Xu, Hongyu Xue, Kim Stanford, Tim A. McAllister, Weiping Xu

Bibliographic record

VenuePLoS ONE · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicComposting and Vermicomposting Techniques
Canadian institutionsAgriculture and Agri-Food CanadaAgriculture Food and Rural Development
FundersDalian University of TechnologyDepartment of Education of Liaoning ProvinceNational Natural Science Foundation of China
KeywordsCompostFood wasteFood scienceManureChemistryChicken manureAmendmentGreen wasteAlkalinityAgronomyBiologyEcology

Abstract

fetched live from OpenAlex

Regular usage of NaOH/NaClO disinfectants results in high sodium salt and alkalinity of poultry manure. This study compared three amendments: vegetable waste (V), food waste (F) and mature compost (C) for their ability to improve the composting of NaOH/NaClO-contaminated poultry manure. C compost resulted in the highest compost temperatures (p<0.001) and greatest reduction in OM, TC, TN and NH4-N (p<0.05). C and V composts were more efficient at lowering extractable-Na (ext-Na) and electrical conductivity (EC) than F (p<0.05). Maturity was primarily indicated by NH4-N, EC and ext-Na. Bacterial dynamics was profoundly influenced by NH4-N, EC and TC, with the decrease leading to discriminate genera shift from Sinibacillus and Thiopseudomonas to Brevbacterium, Brachybacterium, and Microbacterium. These findings suggest that mature compost was more desirable amendment than vegetable and food waste in the composting of NaOH/NaClO-contaminated poultry manure, and the decrease of ext-Na indicated compost maturity but did not influence bacterial dynamics.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.225

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 head0.073
GPT teacher head0.262
Teacher spread0.188 · 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

Citations33
Published2018
Admission routes1
Has abstractyes

Explore more

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