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Record W2752402985 · doi:10.1139/cjce-2017-0168

An investigation on Ontario’s non-hazardous municipal solid waste diversion using trend analysis

2017· article· en· W2752402985 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.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicMunicipal Solid Waste Management
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsHazardous wasteTrend analysisEnvironmental scienceWaste managementMunicipal solid wasteEngineeringStatisticsMathematics

Abstract

fetched live from OpenAlex

Non-hazardous waste disposal and diversion trends in Ontario from 1996 to 2010 were identified using parametric and non-parametric statistical methods, and the temporal variability of its waste diversion practices were examined. Ontario’s diversion was sensitive to waste diversion policy and residential diversion programs. Total waste diversion increased by 85% in 14 years. Results suggested that waste minimization may be more effective than recycling on Ontario diversion rates. Programs targeting non-residential sectors are recommended, specifically for smaller businesses with limited waste management budgets. Linear regression and Mann-Kendall tests detected significant increasing trends for residential waste diversion. In contrast, non-residential diversion had a decreasing trend using linear regression. A significant upward trend (S = +10) was found for Ontario’s total waste diversion using Mann-Kendall tests. Highly significant upward trends were observed for plastic and organic recycling. Mann-Kendall tests were found more appropriate for waste trend analysis in the present study.

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)0.000
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 score0.706
Threshold uncertainty score0.987

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.228
Teacher spread0.205 · 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