MétaCan
Menu
Back to cohort
Record W4231428546 · doi:10.22215/etd/2015-11100

A Quasi-Thermal-Mechanical-Biological Model of the Ste-Sophie, QC Landfill

2015· dissertation· en· W4231428546 on OpenAlexaff
James D. Doyle

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnvironmental Science
TopicLandfill Environmental Impact Studies
Canadian institutionsCarleton University
Fundersnot available
KeywordsMultiphysicsHeat generationThermalEnvironmental scienceThermal conductivityLandfill gasSettlement (finance)Waste heatAnaerobic exerciseNuclear engineeringWaste managementMaterials scienceMechanicsMechanical engineeringThermodynamicsEngineeringComposite materialMunicipal solid wasteFinite element methodPhysicsComputer science

Abstract

fetched live from OpenAlex

A landfill located in Ste-Sophie, QC was instrumented with sensors measuring temperature, oxygen concentration, and settlement.A quasi-thermal-mechanicalbiological conceptual model was developed and a corresponding numerical model was set-up in COMSOL Multiphysics to simulate the temperatures within the vertical waste profile.Thermal conductivity was varied with depth and time in the waste profile.An aerobic heat generation model was proposed that related the aerobic heat generation rate to the oxygen concentration measured near the surface.An anaerobic heat generation model presented in the literature was included in the model.The heat generation rate was dependent on waste temperature and the total energy expended.Corresponding field data were collected and the simulated temperatures were in good agreement with measured field temperatures.A heat budget was computed showing that aerobic and anaerobic heat generation accounted for 36% and 64% of total heat generation, respectively, during the filling stages of landfill operation.

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 score0.836
Threshold uncertainty score0.326

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.037
GPT teacher head0.264
Teacher spread0.227 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

Explore more

Same topicLandfill Environmental Impact StudiesFrench-language works237,207