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Record W2972979055 · doi:10.1177/0734242x19871999

Assessment of the biochemical methane potential of mono- and co-digested dairy farm wastes

2019· article· en· W2972979055 on OpenAlexaff
Mohamad Adghim, Mohamed Abdallah, Suhair A. Gayoum Saad, Abdallah Shanableh, Majid Sartaj

Bibliographic record

VenueWaste Management & Research The Journal for a Sustainable Circular Economy · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicAgriculture Sustainability and Environmental Impact
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMethaneManureChemistryGompertz functionAnimal scienceCow dungAgronomyTotal dissolved solidsEnvironmental scienceFertilizerEnvironmental engineeringBiologyMathematics

Abstract

fetched live from OpenAlex

This study aimed to evaluate the methane potential of mono- and co-digested dairy farm wastes. The tested substrates included manure from lactating, dry, and young cows, as well as waste milk and feed waste. The highest methane yield was achieved from the lactating cow manure, which produced an average of 412 L of CH 4 kg −1 volatile solids, followed by young and dry cow manures (332 and 273 L of CH 4 kg −1 volatile solids, respectively). Feed and milk yielded an average of 325 and 212 L of CH 4 kg −1 volatile solids, respectively. Co-digesting the manures from lactating and young cows with feed improved methane production by 7%. However, co-digesting the dry cow manure with feed achieved only 85% of the calculated methane yield. Co-digesting manure and milk at a ratio of 70:30 enhanced the methane potential from lactating, dry, and young cow manures by 19, 30, and 37%, respectively. Moreover, co-digesting lactating, dry, and young cow manures with milk at a ratio of 30:70 enhanced the methane yield by 60, 30, and 88%, respectively. The cumulative methane production of all samples was accurately described using the Gompertz model with a maximum error of 10%. Carbohydrates contributed the most to methane potential, while proteins and lipids were limiting.

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)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.445
Threshold uncertainty score0.460

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
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 integrity0.0000.000
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 head0.012
GPT teacher head0.291
Teacher spread0.279 · 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

Citations29
Published2019
Admission routes1
Has abstractyes

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