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Record W4246853569 · doi:10.22215/etd/2017-12089

The impacts of temperature and thermal properties on municipal solid waste stabilization

2017· dissertation· en· W4246853569 on OpenAlexaffabout
Courtney Berquist

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnvironmental Science
TopicLandfill Environmental Impact Studies
Canadian institutionsCarleton University
Fundersnot available
KeywordsSettlement (finance)BiodegradationWaste managementMunicipal solid wasteEnvironmental scienceEnvironmental engineeringGeotechnical engineeringEngineeringChemistry

Abstract

fetched live from OpenAlex

Instrument bundles placed within the Ste.Sophie landfill (Quebec, Canada) have been collecting temperature, settlement, and oxygen data since 2009.The temperature and settlement of the landfill were modelled using the finite element software COMSOL Multiphysics.The COMSOL optimization module was utilized to determine the thermal conductivity and specific heat of the waste as a function of time and depth.An anaerobic heat generation coefficient was employed to decrease the overall heat generated in the domain and a new latent heat value was assumed.The concept of total expended energy was used to replace the time-dependent biodegradation-induced settlement term with a temperaturedependent biodegradation-induced settlement term.The new term better accounts for the delayed biodegradation process observed in cold climate wastes.The model was in good agreement with the temperature and settlement data collected from the Ste.Sophie landfill.A simulation was run in order to study the effects of placement conditions on waste settlement and stabilization.It was found that waste placement temperature has an impact on overall settlement at the landfill.By strategically placing waste throughout the year, more waste can be placed during the filling stages of the landfill, simultaneously bringing increased revenues to landfill operators while decreasing the environmental burden of landfills.Mom, Dad, Melissa and Justin-thank you for loving me through the stresses and headaches of my many, many years of university education.Last but not least, I would like to thank Marcel for his support, encouragement, and endless love, and for keeping a smile on my face throughout my undergraduate and graduate degrees.iii 6.2 Regular waste lift sequence modelled expended energies. . . . . . . . . .6.3 Warm waste lift 2 sequence modelled expended energies. . . . . . . . . .6.4 Comparison of simulated expended energies for actual waste lift sequence and warm waste lift 2 sequence. . . . . . . . . . . .

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score0.177

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 studies0.0010.000
Scholarly communication0.0010.000
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 head0.013
GPT teacher head0.254
Teacher spread0.241 · 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

Citations6
Published2017
Admission routes2
Has abstractyes

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