MétaCan
Menu
Back to cohort
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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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 categoriesInsufficient payload (model declined to judge)
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 score0.336
Threshold uncertainty score0.999

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)0.0010.000

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