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Record W4230488217 · doi:10.32920/ryerson.14660367

A study on the potential for sustainable waste management in the Greater Toronto Area

2021· preprint· en· W4230488217 on OpenAlexaffabout
Md. Shamsul Alam

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicMunicipal Solid Waste Management
Canadian institutionsToronto Metropolitan UniversityMuscular Dystrophy Canada
Fundersnot available
KeywordsReuseWaste managementBusinessEnvironmental scienceMunicipal solid wasteEnvironmental planningEngineering

Abstract

fetched live from OpenAlex

Performance of the residential waste management practices in the Greater Toronto Area (GTA) was studied. The study encompassed identification of waste management practices and analysis of data concerning different management options followed by the Regional Municipalities of Durham, Halton, Peel, York and the City of Toronto during 2002 to 2008. Historically, wastes from the GTA were disposed of in the landfills. Majority wastes [sic] from the GTA were exported to Michigan under a contract which is going to be expired [sic] at the end of 2010. Residents already [sic] opposed to accept new landfills. Toxic emissions from the incinerators are also of great concern to them. Integrated waste management system comprising source reduction, recycling and reuse, diversion through green bin SSO program and the aerobic/anaerobic processing of organic waste treatment can be considered to succeed in achieving the most effective and sustainable solution to the residential waste management problems in the GTA.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.198
Threshold uncertainty score0.398

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.028
GPT teacher head0.263
Teacher spread0.236 · 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 designObservational
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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