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Record W2892769331 · doi:10.1139/facets-2018-0005

The role of management instruments in the diversion of organic municipal solid waste and phosphorus recycling

2018· article· en· W2892769331 on OpenAlexaffvenueabout
Jillian L. Treadwell, Elena M. Bennett, O. Grant Clark

Bibliographic record

VenueFACETS · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicMunicipal Solid Waste Management
Canadian institutionsMcGill University
Fundersnot available
KeywordsIncinerationIncentiveBiodegradable wasteLegislatureMunicipal solid wasteWaste managementBusinessEnvironmental scienceEngineeringEconomics

Abstract

fetched live from OpenAlex

Organic waste, which contains essential plant nutrients such as phosphorus, constitutes 30%–50% of municipal solid waste in developed countries. Unfortunately, much of this resource is buried in landfills or incinerated. Many jurisdictions have, therefore, adopted the diversion of organic waste and the recycling of nutrients as policy goals. We used data sets from Europe and Ontario, Canada, to explore the impact of socio-economic and management factors on the rates of organic waste diversion and examined the effect of this diversion on phosphorus recycling. Organic diversion rates were highly correlated with income in Europe and with infrastructure, such as source-separated organic waste collection, in Ontario. Significant correlations were also observed between diversion rates and the use of policy instruments such as economic incentives, legislative organic waste bans, and curbside bag limits. We estimated that 39%–63% of the phosphorus in diverted organics is returned to arable land. Ultimately, we found that although socio-economic factors influence the success of organic waste diversion, policies, accessible infrastructure, economic incentives, and legislative requirements can be leveraged to improve the recycling rate of organic waste and the nutrients they contain.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.517
Threshold uncertainty score0.239

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.001
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 head0.008
GPT teacher head0.227
Teacher spread0.219 · 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 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

Citations4
Published2018
Admission routes3
Has abstractyes

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