MétaCan
Menu
Back to cohort
Record W4248824751 · doi:10.32920/ryerson.14664867.v1

The application of best available technology in dealing with Ontario's waste electric and electronic equipment : a case study

2021· preprint· en· W4248824751 on OpenAlexaboutno aff
Jonathan Pryshlakivsky

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicExtraction and Separation Processes
Canadian institutionsnot available
Fundersnot available
KeywordsElectronic equipmentProfitability indexRevenueIncentiveElectronic wasteElectronicsVolatility (finance)BusinessEnvironmental economicsInvestment (military)Profit (economics)Operations managementEngineeringWaste managementFinanceEconomicsElectrical engineering

Abstract

fetched live from OpenAlex

This study seeks to inquire into the impacts of pursuing a comprehensive Waste Electrical and Electronic Equipment (WEEE) program similar to the system in the European Union in the Province of Ontario. O. Reg. 393/04 WEEE seeks to establish a weight-based system of recycling end-of-life (EOL) electronics. BAT revenue projections would make for a profitable endeavour across the first five years of the program, with reductions in pollution and operating costs from primary ore refinement. Sensitivity analysis reveals that the BAT scheme profitability exceeds the "do nothing" option across all price ranges (including worst case scenarios), while, at the same time, results in increased susceptibility to market volatility. A cost-effectiveness study showed that the investment in a new integrated smelting operation would still be more cost-effective than the "do nothing" option. This study points to the need for further research into market incentives regarding the amount of collected electronic waste.

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 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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.466
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.011
GPT teacher head0.245
Teacher spread0.234 · 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 teacher head, 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 routes1
Has abstractyes

Explore more

Same topicExtraction and Separation ProcessesFrench-language works237,207