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Inkjet Printing and the Clean Air Act

2004· article· en· W4378446525 on OpenAlexaff
Steven Noble, Judith Zaczkowski

Bibliographic record

VenueTechnical programs and proceedings/Technical program and proceedings · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicRecycling and Waste Management Techniques
Canadian institutionsOntario Medical Association
Fundersnot available
KeywordsClean Air ActHazardous air pollutantsAir quality indexHazardous wasteAir pollutantsEnvironmental scienceInkjet printingPollutantWaste managementAir pollutionEnvironmental engineeringInkwellEngineeringComputer scienceMeteorologyChemistry

Abstract

fetched live from OpenAlex

Inkjet printing is widely used to output images. While many believe inkjet to be a green technology, there are environmental issues associated with its use — primarily emissions to air. The Clean Air Act regulates the emissions of volatile organic compounds (VOCs) and hazardous air pollutants (HAPs). Both categories of substances are found in inkjet ink systems. To determine whether air regulations impact their operations, all inkjet printing facilities should calculate their total emissions of VOCs and HAPs. These emissions include both potential-to-emit (based on maximum operating capabilities of the equipment and facility) and actual emissions (based on actual operation conditions). Only by making these calculations and comparing the findings to the local regulations, can a digital printer determine their regulatory compliance requirements. Regulations vary across the U.S., based on the quality of the air in the specific geographic location.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.066
Threshold uncertainty score0.132

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0040.005
Scholarly communication0.0060.003
Open science0.0020.004
Research integrity0.0150.012
Insufficient payload (model declined to judge)0.0130.011

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 head0.011
GPT teacher head0.248
Teacher spread0.237 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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
Published2004
Admission routes1
Has abstractyes

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