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Record W2005731220 · doi:10.1080/03601230903000693

Treating solid dairy manure using microwave-enhanced advanced oxidation process

2009· article· en· W2005731220 on OpenAlexafffund
Anju A. Kenge, Ping Liao, K.V. Lo

Bibliographic record

VenueJournal of Environmental Science and Health Part B · 2009
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture, Soil, Plant Science
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsManureProcess (computing)MicrowaveEnvironmental scienceAgricultural engineeringWaste managementBusinessBiotechnologyProcess engineeringAgronomyComputer scienceEngineeringBiologyTelecommunications

Abstract

fetched live from OpenAlex

The microwave enhanced advanced oxidation process (MW/H(2)O(2)-AOP) was used to treat separated solid dairy manure for nutrient release and solids reduction. The MW/H(2)O(2)-AOP was conducted at a microwave temperature of 120 degrees C for 10 minutes, and at three pH conditions of 3.5, 7.3 and 12. The hydrogen peroxide dosage at approximately 2 mL per 1% TS for a 30 mL sample was used in this study, reflecting a range of 0.53-0.75 g H(2)O(2)/g dry sludge. The results indicated that substantial quantities of nutrients could be released into the solution at pH of 3.5. However, at neutral and basic conditions only volatile fatty acids and soluble chemical oxygen demand could be released. The analyses on orthophosphate, soluble chemical oxygen demands and volatile fatty acids were re-examined for dairy manure. It was found that the orthophosphate concentration for untreated samples at a higher % total solids (TS) was suppressed and lesser than actual. To overcome this difficulty, the initial orthophosphate concentration had to be measured at 0.5% TS.

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)0.000
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 score0.001
Threshold uncertainty score0.001

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.025
GPT teacher head0.293
Teacher spread0.268 · 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

Citations24
Published2009
Admission routes2
Has abstractyes

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Same venueJournal of Environmental Science and Health Part BSame topicAgriculture, Soil, Plant ScienceFrench-language works237,207