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Record W2270007581 · doi:10.1680/jees.15.00003

Effect of hydrogen peroxide on radiofrequency-oxidation of dairy manure

2015· article· en· W2270007581 on OpenAlexafffundvenue
Asha Srinivasan, Chris Young, Ping Liao, K.V. Lo

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicPhosphorus and nutrient management
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHydrogen peroxideManureStruviteChemistryChemical oxygen demandAnaerobic digestionPhosphorusNutrientTotal dissolved solidsPulp and paper industryAgronomyBiochemistryEnvironmental engineeringOrganic chemistryEnvironmental scienceWastewater

Abstract

fetched live from OpenAlex

A novel technology involving a combination of radiofrequency (RF) heating and hydrogen peroxide (H 2 O 2 ) addition was used to treat dairy manure as a part of a manure management and resource recovery scheme; the process was to release nutrients and to disintegrate dairy manure for subsequent struvite crystallisation process and/or anaerobic digestion. The factors affecting the process, namely, H 2 O 2 dosage, heating time and radiofrequency temperature, were studied using a surface response design methodology. The results indicated that solids disintegration efficiency in terms of soluble chemical oxidation demand concentration in the treated solution was in a range of 15–18% of total chemical oxygen demand. Very high volatile fatty acid concentrations were also produced. Orthophosphate (ortho-P) concentrations were in the range of 36–52% of total phosphorus in the treated solution. The optimal operating conditions for both solids disintegration and ortho-P release were predicted to be RF temperature of 83°C, holding time of 55–60 min and H 2 O 2 dose of 1.7–1.8%.

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 score0.034
Threshold uncertainty score0.301

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 head0.004
GPT teacher head0.189
Teacher spread0.184 · 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

Citations2
Published2015
Admission routes3
Has abstractyes

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