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Record W1965880372 · doi:10.1080/09593330.2012.733416

Biogas generation from in-storage psychrophilic anaerobic digestion

2012· article· en· W1965880372 on OpenAlexafffundabout
David Giard, Denis Choinière, Sébastien Cordeau, Suzelle Barrington

Bibliographic record

VenueEnvironmental Technology · 2012
Typearticle
Languageen
FieldEngineering
TopicAnaerobic Digestion and Biogas Production
Canadian institutionsCegep de Saint HyacintheMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaInternational Society for Pediatric and Adolescent DiabetesGeomembrane Technologies
KeywordsBiogasAnaerobic digestionMethaneEnvironmental scienceManureWaste managementPsychrophileAnaerobic exerciseCarbon dioxideEnvironmental engineeringAgronomyChemistryEngineeringEcologyBiology

Abstract

fetched live from OpenAlex

In-storage psychrophilic anaerobic digestion (ISPAD) is a technology allowing livestock producers to operate an anaerobic digester with minimum technological know-how and for the cost of a conventional storage cover. Nevertheless, the system is exposed to ambient temperatures and biogas production is expected to vary with climatic conditions. The objective of the project was therefore to measure ISPAD biogas production during the winter and fall seasons for a region east of Montreal, Canada. A calibrated biogas monitoring system was used to monitor biogas methane and carbon dioxide concentrations inside a two-year-old field installation with a 1000 m3 storage capacity. Despite a leaking pumping hatch, winter 2010 (January to March) methane concentrations varied directly with solar radiation and maximum exterior temperature, rather than with manure temperature at 2.4 and 1.2 m depths which remained relatively constant between 1 and 5 degrees C. During a six-month-period from November 2009 to April 2010, inclusively, the field ISPAD degraded 34% of the manure volatile solids corresponding to an average methane production of 40 m3/d. The ISPAD biogas production could be further increased by improving its air tightness and intrusion and by regularly pumping out the biogas.

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)0.000
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 score0.528
Threshold uncertainty score0.547

Codex and Gemma teacher scores by category

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 head0.009
GPT teacher head0.187
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 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

Citations9
Published2012
Admission routes3
Has abstractyes

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