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Record W4224314958 · doi:10.3390/app12094276

Experimental Characterization of the Engineering Properties of Landfill Compost-Biocover

2022· article· en· W4224314958 on OpenAlexaff
Tariq Mahmood Bajwa, Mamadou Fall, Fahad Alshawmar

Bibliographic record

VenueApplied Sciences · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicLandfill Environmental Impact Studies
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsCompostHydraulic conductivityEnvironmental scienceMethaneWater contentWaste managementEnvironmental engineeringSoil scienceSoil waterGeotechnical engineeringChemistryEngineering

Abstract

fetched live from OpenAlex

A landfill biocover system optimizes environmental conditions for biotic methane (CH4) consumption that controls the fugitive and residual emissions from landfills. Research shows that wasted compost material has more (CH4) oxidation potential than other materials. Thus, in this study, the authors investigate the engineering properties of compacted compost to test its suitability for CH4 oxidation capacity. Different laboratory and analytical approaches are employed to attain the set objectives. The biochemical tests show that the studied material indicates the presence of methanotrophs with sufficient organic contents. The compacted compost also shows adequate diffusivity potential to free air space for a wide range of water content. The data also imply that compacting compost to low hydraulic conductivity can be accomplished for a wide range of water content, according to the suggested values for a landfill hydraulic barrier. Furthermore, the low thermal properties of compost as compared to other mineral materials seem more beneficial, as specifically, during the winter season, when the atmospheric temperature is low, low thermal conductivity enables it to sustain a stable temperature for the activities of the microbial organisms, which therefore extends the CH4 oxidation process right through a long period in the winter.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.499

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 science0.0000.001
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 head0.183
Teacher spread0.170 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

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