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Record W2132395862 · doi:10.1139/tcsme-2009-0013

INDICATORS FOR THE ENVIRONMENTAL IMPACT OF WASTE EMISSIONS: COMPARISON OF EXERGY AND OTHER INDICATORS

2009· article· en· W2132395862 on OpenAlexafffundvenue
Marc A. Rosen

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Impact and Sustainability
Canadian institutionsOntario Tech University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsExergyEnvironmental scienceEnvironmental indicatorEnvironmental impact assessmentEnvironmental engineeringEnvironmental economicsWaste managementEngineeringEcologyEconomics

Abstract

fetched live from OpenAlex

Exergy is compared with other indicators for the environmental impact of waste emissions in an effort to better understand its potential as, or as the basis for, an effective indicator of the potential of an emitted substance to impact the environment. Relations between environmental impact and exergy in general and chemical exergy of waste emissions in particular are observed to support the use of exergy as such an indicator. The measure of disequilibrium with respect to a reference environment provided by exergy is considered, along with the consequence that the exergy of unrestricted waste emissions has the potential to impact the environment. Exergy is observed to exhibit many characteristics of other indicators of environmental impact, which are mainly empirical in nature. An exergy-based indicator could contribute to the development of rational and objective procedures for assessing the harmful effects on the environment of a substance and predicting its potential for environmental impact. The potential usefulness of exergy in addressing environmental problems is concluded to be significant, but further research is needed to develop objective exergy-based indicators that are practical for the potential of a substance to impact on the environment.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.239
Threshold uncertainty score0.462

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 head0.008
GPT teacher head0.240
Teacher spread0.232 · 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 designSimulation or modeling
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

Citations9
Published2009
Admission routes3
Has abstractyes

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