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Record W2794870692 · doi:10.1021/acs.est.7b06478

Occurrence of Single- and Double-Peaked Emission Profiles of Synthetic Chemicals

2018· article· en· W2794870692 on OpenAlexaff
Li Li, Frank Wania

Bibliographic record

VenueEnvironmental Science & Technology · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Impact and Sustainability
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
Fundersnot available
KeywordsEnvironmental scienceWork (physics)CategorizationBiochemical engineeringComputer scienceEngineering

Abstract

fetched live from OpenAlex

This work aims to elucidate the circumstances that can lead to two peaks in the temporal emission profile of synthetic chemicals. Using a simplified substance flow model, we explore how emission factors, product lifespan, and degradation half-life in waste stock influence the (i) relative importance of emissions from three lifecycle stages (industrial processes, use phase, and waste disposal), and (ii) the resulting composite emission profile. A double-peaked emission profile occurs if the lifespan of products containing the chemical is longer than its production history, and the gross emission factor from waste disposal exceeds that from the use phase. Since most chemicals fail to meet these two conditions, it is reasonable to use single-peaked emission profile as the default in environmental studies. On the basis of their emission profiles and contributions from individual lifecycle stages, we can categorize chemicals into "simple single-peakers", "composite single-peakers", and "double-peakers". Our simplified model derived emission profiles for five real chemicals that agree well with earlier, more sophisticated calculations, indicating the model's ability to capture the essential features of actual emissions. It is hoped that the model and conclusions in this work will benefit both environmental modelers and decision makers.

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 categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.163
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.019
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.240
Teacher spread0.231 · 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 designBench or experimental
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

Citations17
Published2018
Admission routes1
Has abstractyes

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