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Record W1993748355 · doi:10.1139/s08-001

Feasibility of increased biogas production from the co-digestion of agricultural, municipal, and agro-industrial wastes in rural communities

2008· article· en· W1993748355 on OpenAlexafffundvenueabout
Tanya McDonald, Gopal Achari, Abimbola Abiola

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2008
Typearticle
Languageen
FieldEngineering
TopicAnaerobic Digestion and Biogas Production
Canadian institutionsUniversity of CalgaryOlds College
FundersFederation of Canadian Municipalities
KeywordsAnaerobic digestionBiogasManureFeedlotEnvironmental scienceNutrientBioenergyAgricultureWaste managementBiodegradable wastePulp and paper industryBiofuelAgronomyMethaneAnimal scienceChemistryBiologyEngineering

Abstract

fetched live from OpenAlex

In rural communities, such as Olds, Alberta (population 7248) energy may be produced through anaerobic co-digestion of municipal, agricultural, and agro-industrial wastes. An inventory within a 20 km radius of Olds revealed that 291 000 tonnes of wet organic waste are generated annually (96.8% manure) with an electrical potential of 42 GWh (1 GWh = 106 kWh). Analysis of feedlot, hog, dairy and poultry manure, offal, food, grass, and biosolids identified the potential for waste blending to optimize solids content, nutrient balance, and pH level. In laboratory tests, biogas yield increased from 0.382 m3/kg volatile solid (VS) for feedlot manure alone to 0.513, 0.517, and 0.500 m3/kg VS with co-digestion of 30% hog manure, 15% offal, and 30% offal, respectively. Methane content of biogas increased from 75.8% in feedlot manure digestion to 78.8, 79.5, and 80.9% with co-digestion of 30% hog manure, 15% offal, and 30% offal, respectively.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.035

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 communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.023
GPT teacher head0.202
Teacher spread0.179 · 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 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

Citations10
Published2008
Admission routes4
Has abstractyes

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