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Record W2885952949 · doi:10.22507/pml.v12n2a1

Algunas variables para la evaluación de tecnologías aislantes óptimas en la implementación en cajas refrigerantes portátiles. Corporación Universitaria Lasallista

2017· article· es· W2885952949 on OpenAlexaff
Ana Cristina Zúñiga Zapata, Gilmar Sáenz Tejada, Luis Gómez, Steven Angel

Bibliographic record

VenueProducción + Limpia · 2017
Typearticle
Languagees
FieldEconomics, Econometrics and Finance
TopicBusiness, Innovation, and Economy
Canadian institutionsHatch (Canada)
Fundersnot available
KeywordsHumanitiesArtPhilosophy

Abstract

fetched live from OpenAlex

Introducción. El transporte de productos perecederos es un tema que amerita investigación, con el fin de brindar elementos a las pequeñas y medianas empresas para mejorar su cadena logística en el transporte nacional e internacional. Objetivo. El objetivo del presente artículo es presentar los resultados derivados del análisis de variables usadas en el diseño de una caja refrigerante para transporte de biológicos. Materiales y métodos. Este trabajo se presenta como un artículo original derivado de la investigación generada en la Corporación Universitaria Lasallista, en el marco del proyecto “Fortalecimiento de las capacidades de transferencia, comercialización y valoración de tecnologías”, financiado por Innpulsa Colombia en el año 2015. Resultados. El trabajo contiene un análisis de tecnologías sustitutas, además de una síntesis de algunas de las características de la tecnología desarrollada para satisfacer las necesidades del mercado. Conclusión. El resultado de la investigación brinda información acerca de las variables adecuadas para el diseño y optimización de la tecnología con miras a obtener el equilibrio adecuado entre la conservación, la trazabilidad y la autonomía.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.329
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.029
GPT teacher head0.283
Teacher spread0.254 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2017
Admission routes1
Has abstractyes

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