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Record W4221022406 · doi:10.1061/9780784484050.012

Field Temperatures and Geothermal Modeling of an MSW Landfill Located in Humid Climate

2022· article· en· W4221022406 on OpenAlexaff
Milind V. Khire, Terry R. Johnson, W. C. B. Meyer, Richard I. G. Holt

Bibliographic record

VenueGeo-Congress 2022 · 2022
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsNuclear Waste Management Organization
Fundersnot available
KeywordsLeachateGeothermal gradientMunicipal solid wasteEnvironmental scienceWaste managementWaste heatCombustionInfiltration (HVAC)CoalEnvironmental engineeringMaterials scienceGeologyHeat exchangerChemistry

Abstract

fetched live from OpenAlex

While most municipal solid waste (MSW) landfills maintain temperatures below 55°C (131°F), a relatively few MSW landfills have temperatures exceeding 93°C (200°F). In order to understand the key mechanisms that allow heat accumulation and temperature rise, commercial geothermal model TETRAD was used to simulate the heat transfer in an MSW landfill located in the southeastern United States. Field temperatures of the landfill were monitored using thermistor sensor arrays. The measured peak temperatures ranged from 68°C to 77°C (155°F to 170°F). Waste heat generation, infiltration, and leachate flow were the key variables evaluated in the modeling. The model results indicated that the average waste heat generation rates for this landfill ranged from 0.3 to 1.4 W/m3. The higher heat generation rate corresponds to a relatively young (≤4 years) portion of waste containing a relatively high mass fraction of coal combustion fly ash. The lower heat generation rate corresponds to a 14-year old portion of waste predominantly containing MSW.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.072

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.010
GPT teacher head0.239
Teacher spread0.229 · 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 designSimulation or modeling
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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