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Record W3210491960 · doi:10.32920/ryerson.14654991.v1

A production-recycling-reuse model for plastic beverages bottles

2021· preprint· en· W3210491960 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicMedicinal Plant Studies
Canadian institutionsToronto Metropolitan University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsReuseWaste managementPolyethylene terephthalateProduction (economics)Profit (economics)Production costEnvironmental scienceBusinessPulp and paper industryEngineeringMaterials scienceEconomicsComposite material

Abstract

fetched live from OpenAlex

In this thesis a recycling-reuse model is developed and analyzed. Discarded 2L plastic PET (polyethylene terephthalate) bottles are collected from the market. The non-contaminated PET bottles are either remanufactured or used as regrind mixed with virgin PET to produce new bottles to satisfy varying demand. Contaminated bottles are sold to industries using low grade plastic and only badly contaminated bottles go to landfill. Cost of land use and associated environmental damage is calculated as a present worth and charged to the manufacture. Analyses conducted on this model found that the amount of bottles collected had the largest influence on the outcome of the total system unit time cost. Alternative materials to PET that degrade faster are surveyed and used to demonstrate significant reduction in the cost of landfill disposal. Analysis using a minimal market price for remanufactured and newly produced bottles resulted in profit.

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)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.818
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.082
GPT teacher head0.335
Teacher spread0.252 · 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

Quick stats

Citations0
Published2021
Admission routes2
Has abstractyes

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