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Record W4406756110 · doi:10.3390/app15031136

Life Cycle Assessment of Proofing Test Production on Printing Surfaces with Use of Carbon Footprint Methodology

2025· article· en· W4406756110 on OpenAlexaff
Jacek Nogacki, Urban Buschmann, Krzysztof Krystosiak, Zuzanna Żołek‐Tryznowska

Bibliographic record

VenueApplied Sciences · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicRecycling and Waste Management Techniques
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsCarbon footprintLife-cycle assessmentProduction (economics)Environmental scienceGreenhouse gasGeologyOceanographyEconomics

Abstract

fetched live from OpenAlex

This study represents a pioneering initiative in the printing industry, especially in Poland, which assessed the environmental impacts and eco-efficiency of proof printing through the life cycle assessment (LCA) methodology. The process of proof printing on a target substrate was compared with the traditional hard proofing process, which requires trial printing in production conditions. The analysis adhered to the ISO 14040 and 14044 standards, assessing greenhouse gas (GHG) emissions, raw material use (e.g., plastics, water), and environmental toxicity. The innovative proofing on the target substrate process exhibits a lower environmental impact, as confirmed by the GHG emissions and plastic and water demand of the process. The GHG emissions were reduced from 2610 kg of CO2e to 68.4 kg of CO2e per functional unit (FU). The water demand for the proofing on the target substrate process was 40 times lower, and the plastic demand was also 40 times lower, decreasing to 20 kg per FU. The toxicity impact of the method based on the proofing system on the target substrate on living organisms is more than six times lower than that of the conventional method. The proof printing on the target substrate process offers an environmentally friendly alternative to the traditional hard proofing process, with lower GHG emissions and a lower environmental impact.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.332
Threshold uncertainty score0.272

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.059
GPT teacher head0.321
Teacher spread0.261 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations3
Published2025
Admission routes1
Has abstractyes

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