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Record W2335834927

Opportunities for reducing volatile organic compound emissions in manufacturing office furniture partitions: a feasibility analysis

2009· article· en· W2335834927 on OpenAlexaffabout
Frank S. Luisser, Marc A. Rosen

Bibliographic record

VenueInternational Conference on Energy & Environment · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicIndoor Air Quality and Microbial Exposure
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsChristian ministryPollution preventionVolatile organic compoundPlan (archaeology)Control (management)Waste managementFurniture industryBaseline (sea)Environmental scienceEnvironmental economicsComputer scienceOperations managementManufacturing engineeringEngineering
DOInot available

Abstract

fetched live from OpenAlex

A feasibility analysis is reported of reduction opportunities for volatile organic compound (VOC) emissions in manufacturing office furniture partitions. The pollution prevention (P2) methodology as defined by the Ontario Ministry of the Environment is utilized. The purpose is to provide a VOC P2 plan for manufacturing office furniture partitions, but the concepts can be generally applied to the wood furniture industry. Baseline VOC emissions for a typical plant are estimated using a mass balance approach. The feasibility analysis expands on a preliminary screening to identify viable P2 options using realistic criteria and weightings, and is based on technical, environmental and economic considerations. The measures deemed to be feasible include the implementation of several best management practices, ceasing the painting of non-visible parts, switching to hot melt backwrapping glue, application of solvent recycling and modification of the mechanical clip attachment. Implementation, measurement and control plans are discussed for the measures considered feasible.

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 categoriesInsufficient payload (model declined to judge)
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 score0.575
Threshold uncertainty score0.992

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)0.0090.000

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 head0.066
GPT teacher head0.279
Teacher spread0.213 · 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 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

Citations1
Published2009
Admission routes2
Has abstractyes

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