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Inkjet Printing and the Clean Air Act

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

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.888
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.003
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.001
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 head0.011
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