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Record W4362632681 · doi:10.21203/rs.3.rs-2612369/v1

Radical-induced rapid humification of waste milk and the performance of derived slow release fulvic-like fertilizer

2023· preprint· en· W4362632681 on OpenAlexaff
Yanping Zhu, Yuxuan Cao, Chengjin Wang, Shihu Shu, Jinpeng Zhu, Dongfang Wang, He Xu, Dongqing Cai

Bibliographic record

VenueResearch Square · 2023
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicComposting and Vermicomposting Techniques
Canadian institutionsUniversity of Manitoba
FundersFundamental Research Funds for the Central UniversitiesNational Natural Science Foundation of China
KeywordsHumusChemistryFertilizerFulvic acidEnvironmental chemistryResource recoveryPersulfateWastewaterStrawGreen wasteEnvironmental pollutionHumic acidPulp and paper industryWaste managementEnvironmental scienceOrganic chemistryEnvironmental engineeringInorganic chemistrySoil waterCatalysis

Abstract

fetched live from OpenAlex

Abstract The direct disposal of waste milk (WM) leads to severe environmental pollution and resource loss. Considering the high content of nutrients, WM has a potential as an ideal raw material for organic fertilizer. In this work, base-activated persulfate (KOH/PS) was used as a new artificial humification technology to transform WM into product with 45.3% of fulvic-like acid (FLA) and 18.9% of humic-like acid (HLA) in 1 hour. Therein, FLA had more active groups (-COOH, -CNOH, -OH) than natural fulvic acid likely owing to hydroxylation, carboxylation and the Millard reaction. Reactive species of •OH and SO 4 − • generated in KOH/PS system may be related to degradation or polymerization reactions during humification. The product was mixed with attapulgite to fabricate a slow-release nano FLA fertilizer which could increase the yield of chickweeds by 107% compared with the blank as well as the abundance of beneficial bacteria in soil. Overall, this study provided a rapid method for the recycling of waste food and highly-concentrated organic wastewater, which may have a huge application prospect in sustainable agriculture.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.350
Threshold uncertainty score0.342

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.110
GPT teacher head0.340
Teacher spread0.230 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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