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Record W2163345603 · doi:10.1155/2010/573149

The Use of Inorganic Packing Materials during Methane Biofiltration

2010· article· en· W2163345603 on OpenAlexafffund
Josiane Nikiema, Michèle Heitz

Bibliographic record

VenueInternational Journal of Chemical Engineering · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicLandfill Environmental Impact Studies
Canadian institutionsUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiofilterPorosityMethaneParticle sizeMaterials scienceParticle (ecology)Specific surface areaChemical engineeringMineralogyWaste managementComposite materialEnvironmental engineeringEnvironmental scienceChemistryGeologyOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

The objective behind this study is to select a suitable inorganic packing material for methane biofiltration. Three packing materials are to be compared: two rock materials (average particles' sizes: 2 and 5 mm) and one porous clay particles (average particle size of 7 mm). The main parameter used to assess the efficiency of the packing material is the methane elimination capacity. The study reveals that the rock material having an average particle size around 2 mm is to be preferred. This result is probably due to its high specific surface area and to its good surface properties as compared to the other 2 tested porous materials. The influence of the nonirrigation with the nutrient solution of the biofilter is also investigated. It has been found that nonirrigation of biofilter causes the biofilter performance to decrease significantly (e.g., 45% decrease in 1 week) even with the humidification of the gas phase prior to its introduction into the biofilter.

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)0.001
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 score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0000.000
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 head0.214
Teacher spread0.205 · 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

Citations28
Published2010
Admission routes2
Has abstractyes

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