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Record W2051722642 · doi:10.3155/1047-3289.61.7.771

Nitrogen Conservation in Simulated Food Waste Aerobic Composting Process with Different Mg and P Salt Mixtures

2011· article· en· W2051722642 on OpenAlexaff
Yu Li, Bensheng Su, Jianlin Liu, Xianyuan Du, Guohe Huang

Bibliographic record

VenueJournal of the Air & Waste Management Association · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicPhosphorus and nutrient management
Canadian institutionsUniversity of Regina
FundersChina Scholarship Council
KeywordsFood wasteBiodegradationChemistryPotassiumSalt (chemistry)StruviteNitrogenAmmoniaMagnesiumPhosphateSulfateNuclear chemistryWaste managementFood scienceOrganic chemistry

Abstract

fetched live from OpenAlex

To assess the effects of three types of Mg and P salt mixtures (potassium phosphate [K3PO4]/magnesium sulfate [MgSO4], potassium dihydrogen phosphate [K2HPO4]/MgSO4, KH2PO4/MgSO4) on the conservation of N and the biodegradation of organic materials in an aerobic food waste composting process, batch experiments were undertaken in four reactors (each with an effective volume of 30 L). The synthetic food waste was composted of potatoes, rice, carrots, leaves, meat, soybeans, and seed soil, and the ratio of C and N was 17:1. Runs R1-R3 were conducted with the addition of K3PO4/ MgSO4, K2HPO4/MgSO4, and KH2PO4/MgSO4 mixtures, respectively; run R0 was a blank performed without the addition of Mg and P salts. After composting for 25 days, the degrees of degradation of the organic materials in runs R0-R3 were 53.87, 62.58, 59.14, and 49.13%, respectively. X-ray diffraction indicated that struvite crystals were formed in runs R1-R3 but not in run R0; the gaseous ammonia nitrogen (NH3-N) losses in runs R0-R3 were 21.2, 32.8, 12.6, and 3.5% of the initial total N, respectively. Of the tested Mg/P salt mixtures, the K2HPO4/ MgSO4 system provided the best combination of conservation of N and biodegradation of organic materials in this food waste composting process.

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 score0.013
Threshold uncertainty score0.370

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 head0.008
GPT teacher head0.186
Teacher spread0.178 · 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

Citations12
Published2011
Admission routes1
Has abstractyes

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