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Record W4221009399 · doi:10.1061/9780784484036.009

Heat Loss in Landfills as a Function of Landfill Size

2022· article· en· W4221009399 on OpenAlexaff
Milind V. Khire, Terry R. Johnson, Richard I. G. Holt

Bibliographic record

VenueGeo-Congress 2022 · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicLandfill Environmental Impact Studies
Canadian institutionsNuclear Waste Management Organization
Fundersnot available
KeywordsEnvironmental scienceLeachateGeothermal gradientVolume (thermodynamics)Heat generationGeotechnical engineeringWaste managementEnvironmental engineeringSoil scienceGeologyThermodynamicsEngineering

Abstract

fetched live from OpenAlex

In order to understand the effect of size of landfill on heat accumulation, heat loss, and resulting temperatures, heat generation and heat loss were simulated using the commercial 3D geothermal simulator TETRAD. Three landfills were simulated each having the same total volume of 16.7 × 106 m3, liner areas ranging from 33.5 ha to 55.8 ha, and total heights ranging from 75 m to 121 m. The shape of all simulated landfills was symmetrical pyramidal frustum. Heat generation in all landfills was simulated by assuming an average waste heat generation rate of 0.5 W per cubic meter of waste. The temperatures of the waste simulated by the model for the first 10 years were plotted for comparison. The peak simulated temperature in these landfills was about the same. However, median temperature or average along a vertical axis passing through the center of landfill was slightly higher for the tallest landfill and slightly lower for the shortest landfill compared to the baseline landfill. This numerical study indicates that, flatter side slopes will have greater heat loss and lower median temperature than landfills that have steeper side slopes.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.005
GPT teacher head0.211
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 designObservational
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

Citations1
Published2022
Admission routes1
Has abstractyes

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