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Record W2084701419 · doi:10.1115/es2010-90395

Resource Accounting and Waste Impact: Limitations of Exergy as a Sustainability Tool

2010· article· en· W2084701419 on OpenAlexaff
Kyrke Gaudreau, Roydon Fraser, Stephen D. Murphy

Bibliographic record

VenueASME 2010 4th International Conference on Energy Sustainability, Volume 1 · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Impact and Sustainability
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsExergySustainabilityContext (archaeology)Environmental economicsProcess (computing)Computer scienceRisk analysis (engineering)Resource (disambiguation)Natural resourceManagement scienceProcess engineeringEngineeringBusinessEconomicsEcology

Abstract

fetched live from OpenAlex

Exergy is a thermodynamic concept that has been widely promoted for assessing and improving sustainability, notably in the characterization of resources and wastes. Despite having notable benefits, exergy is often misused by authors who tend to apply it as an intrinsic characteristic of an object (i.e., as a static thermodynamic property). Using both theoretical and empirical evidence the authors introduce the challenges involved with applying exergy as an intrinsic characteristic matter with particular focus on resource value and waste impact. These challenges lead to an in-depth discussion of current major reference environment formulations and reveals that the properties of exergy reference environments are not reconcilable with the properties of the natural environment. The authors conclude by arguing that exergy practitioners should abandon attempts to formulate standard comprehensive reference environments and return to process dependent reference environments that exergy was originally based upon. In this regard, the authors are proposing that exergy be seen as a context- or environment-dependent decision-making tool and not as an intrinsic characteristic of matter.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.128
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.017
GPT teacher head0.272
Teacher spread0.256 · 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.

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

Citations1
Published2010
Admission routes1
Has abstractyes

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