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Record W7009396093

Economic Analysis for Thermal Treatment of Wastepaper in Saskatchewan

2022· dissertation· en· W7009396093 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity Library (University of Saskatchewan) · 2022
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicMycorrhizal Fungi and Plant Interactions
Canadian institutionsnot available
Fundersnot available
KeywordsEconomic analysisEconomic feasibilityPopulationCapital costProduction (economics)Thermal power stationIncinerationEconomic evaluationEconomic cost
DOInot available

Abstract

fetched live from OpenAlex

Changing recycling markets and fluctuating market prices for wastepaper are increasing the financial risks for Saskatchewan municipalities and their waste disposal practices. Landfilling is generally considered the lowest cost alternative while recycling, although considered to be the right thing to do environmentally, can be cost prohibitive. The increasing demand for non-fossil fuel-based energy sources may present an opportunity for municipalities to economically generate heat and in some cases, power, through thermal treatment of wastepaper utilizing incineration with heat recovery or combined heat and power units. Barriers to the adoption are the upfront financial cost and lack of consolidated information required to perform a feasibility analysis. This thesis consolidates the information required for municipalities to conduct a first-pass feasibility analysis. Supporting information and data was collected to provide: a summary of technology, referenced values for the availability of wastepaper, capital costs for construction of thermal treatment plants, and expected values for heat and power production and sales. This data is intended for use by municipalities to evaluate thermal treatment of wastepaper. To assist with the evaluation, a Decision Support Tool (DST) was then developed to provide an automated economic evaluation of thermal treatment plants operating on wastepaper based on inputs from the user (municipality) and from available literature. Saskatoon, Swift Current, Outlook, and La Loche in Saskatchewan, Canada, were used as case studies for the evaluation of wastepaper thermal treatment in Saskatchewan. Saskatoon is the largest city in Saskatchewan with a population of approximately 250,000. Swift Current is the mid-point of the ten largest Saskatchewan municipalities, approximately 17,000. Outlook is the tenth largest municipality, approximately 2,300. La Loche, approximate population of 2,300 was selected to evaluate a Northern community. The selected municipalities also serve to evaluate a large population range within Saskatchewan with approximately an order of magnitude difference between each municipality. Using the developed DST for the case studies indicated that thermal treatment of wastepaper in the four locations has the potential to provide a disposal cost lower than recycling. In all cases, capital cost was the main driver, with operating and maintenance costs as the secondary driver, of the viability of thermal treatment of wastepaper compared to landfilling or recycling.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.202
Threshold uncertainty score0.407

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0040.006
Science and technology studies0.0020.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.007
GPT teacher head0.172
Teacher spread0.165 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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