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Record W7083594326 · doi:10.1016/j.eti.2025.104549

Quantification of improperly disposed mercury containing lights in Canadian households

2025· article· en· W7083594326 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueEnvironmental Technology & Innovation · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic, financial, and policy analysis
Canadian institutionsUniversity of Regina
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMercury (programming language)MERCURY EXPOSUREAir pollutionMercury contamination

Abstract

fetched live from OpenAlex

Mercury containing lamps (MCL), often classified as household hazardous waste, are a threat to human and environmental health. Extended Producer Responsibility (EPR) is a common management framework. The objectives of this study are to estimate the improper disposal of household MCLs across eight Canadian provinces (with and without EPR) and model households’ improper MCL disposal using seven factors. The number of retail locations collecting lamps per 100,000 households is about 10 in provinces with EPR, and about 3 in provinces without EPR. Across all study areas, MCLs being disposed of in landfills are decreasing over time. Pearson correlation analysis found that environmental awareness and socio-economic factors are more important to electronic light usage in provinces with EPR. Regression analysis showed that the percentage of households using LEDs was important in determining the disposal of MCLs in provinces with and without EPR. For provinces with existing EPR programs for MCLs, the percentage of households composting kitchen waste was important. This may be due to household members being more environmentally aware. In non-EPR provinces, obtaining a tertiary education was important in determining the disposal of MCLs. EPR is an effective tool in the management of MCL waste in Canada. The conventional MCL per capita variable may be less useful given the changing household size. We recommend monitoring and reporting of the MCL quantity at the household level for policy-making purposes.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.362
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.016
GPT teacher head0.217
Teacher spread0.201 · 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