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Record W2072216861 · doi:10.1115/nawtec19-5439

Changes in Public Perception of Role of Waste-to-Energy for Sustainable Waste Management of MSW

2011· article· en· W2072216861 on OpenAlexaboutno aff
Nickolas J. Themelis

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicNuclear and radioactivity studies
Canadian institutionsnot available
Fundersnot available
KeywordsWaste-to-energyGovernment (linguistics)ChinaSustainable managementCleaner productionBusinessWaste managementSustainable developmentEngineeringEnvironmental planningMunicipal solid wasteEconomic growthPolitical scienceSustainabilityEnvironmental scienceEconomicsLaw

Abstract

fetched live from OpenAlex

In the last ten years, public and government perceptions of waste-to-energy have changed considerably. Most people who bothered to visit waste management facilities recognize that landfilling can only be replaced by a combination of recycling and thermal treatment with energy recovery. During the same period, the Earth Engineering Center (EEC) of Columbia University research and public information programs have concentrated on advancing all means of sustainable waste management in the U.S. and abroad. The results of EEC research are exemplified in the graphs of the Hierarchy of Waste Management and the Ladder of Sustainable Waste Management of nations; in this paper, the latter has also been used to compare the waste management status of the fifty states of the Union. This paper also describes how the European Union has directed that thermally efficient treatment of MSW is equivalent to recycling. The rapid growth of WTE in this century is exemplified by the hundreds of new WTE plants that have been built or are under construction, most with, government assistance as in the case of other essential infrastucture. The need for concerted action by concerned scientists and engineers around the world has led to the formation of the Global WTERT Council. By now there are sister organizations of EEC and WTERT in Brazil, Canada, China, France, Germany, Greece (SYNERGIA) and Japan. Others are being formed in other countries.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.536
Threshold uncertainty score0.229

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 integrity0.0000.000
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 head0.201
Teacher spread0.184 · 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 designQualitative
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
Published2011
Admission routes1
Has abstractyes

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Same topicNuclear and radioactivity studiesFrench-language works237,207