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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 OpenAlexafffund
Nouri Dawood Matar

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.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.028
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.082
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

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
Published2021
Admission routes2
Has abstractyes

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