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Record W2975522312 · doi:10.5539/jas.v11n17p158

Evaluation of CO2 Emission From Rice Husk Biochar and Cowdung Manure Co-compost Preparation

2019· article· en· W2975522312 on OpenAlexvenueno aff
EC Thomas, S.G.K. Adiku, C. J. Atkinson, J. A. I. Omueti, D. S. Marcarthy

Bibliographic record

VenueJournal of Agricultural Science · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicComposting and Vermicomposting Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsHuskCompostBiocharCow dungManureChicken manureEnvironmental scienceGreenhouse gasAnimal scienceAgronomyChemistryPulp and paper industryWaste managementFertilizerPyrolysisBiology

Abstract

fetched live from OpenAlex

Composting of animal manure had been considered a sustainable alternative method for recycling organic waste. However the process involved had been associated with greenhouse gas emission (CO2, N2O and CH4) which play an active role in global warming. This study evaluated CO2 emissions from biochar-manure co-compost production. Biochar (from rice husk) and manure were mixed in a ratio of 3:1 v/v to achieve a range of different co-compost mixtures. The treatments and controls in triplicates of 18 units were arranged in a complete randomize design. All treatments were incubated at around 28 oC and turned every two days for 2 weeks, and later five days for 39 days. CO2 production in the compost bins was measured by trapping the evolved gas in 5M NaOH. Total CO2 emissions varied over time with higher rates at the beginning of the composting process. Within the first 7 days, total CO2 emissions (587 mg/m2) from cow dung alone was not significantly different from cow dung plus biochar (506 mg/m2). At the latter stages of the composting process, CO2 emission from cowdung and biochar mixture was less than from the other treatments.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.690
Threshold uncertainty score0.156

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.026
GPT teacher head0.295
Teacher spread0.269 · 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
Published2019
Admission routes1
Has abstractyes

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