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Quantitative and qualitative factors for material selection in coffee packaging design

2023· article· en· W4381193544 on OpenAlex

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

Venuenot available
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Packaging Perceptions and Trends
Canadian institutionsImpact
Fundersnot available
KeywordsConsumption (sociology)Production (economics)Selection (genetic algorithm)Product (mathematics)BusinessMarketingPoint (geometry)Presentation (obstetrics)Industrial organizationQualitative researchComputer scienceManufacturing engineeringEngineeringEconomicsMathematicsSociologyMicroeconomicsArtificial intelligence

Abstract

fetched live from OpenAlex

The selection of materials for coffee packaging is the central point of this article, contextualized in the Brazilian market, the largest producer and one of the largest consumers globally. The ways of packaging presentation to the consumer, the range of audience and the involvement of producers and brands make it possible to illustrate the production and consumption chains and the product life cycle stages. The methodology used is qualitative, starting from bibliographical and documental research, referring to the coffee market and the use of materials in the development of packaging, moving on to the graphic synthesis of the chains to identify stages and flows that allowed indicating factors of influence for managing materials in design projects. As a conclusion, it was pointed out the possibility of replicating these factors to other markets, with greater or lesser 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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.335
Threshold uncertainty score0.394

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 studies0.0000.000
Scholarly communication0.0000.001
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 head0.127
GPT teacher head0.353
Teacher spread0.226 · 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