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Seguridad alimentaria y fortificación de alimentos a base de pulpa de café en tiempos pandémicos

2022· article· W7135221809 on OpenAlexaff
Carmen Luisa Marín Tello, Franklin Fernández-Sánchez, Paola Rodriguez Cruzado, Catherine Salcedo Robles, Cindy Morán, Albert Cerna López, Iván González Puetate, Violeta Cecilia Malpartida Tello, César Sánchez Marín, Lorena Zelada Castillo, Alexander Vásquez Arqueros, Amandio Vieira

Bibliographic record

VenueRevista Colombiana de Ciencias Químico Farmacéuticas · 2022
Typearticle
Language
FieldAgricultural and Biological Sciences
TopicAgriculture and Social Issues
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsFood intakeFeeding behaviorSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Human immunodeficiency virus (HIV)

Abstract

fetched live from OpenAlex

Introducción: la pandemia de COVID-19 originó pérdidas humanas, tensiones en la atención médica, la economía y otros sistemas sociales. Objetivo: recopilar información sobre seguridad alimentaria y fortificación de alimentos a base de pulpa de café considerando que una buena nutrición contrarresta las infecciones. Metodología: se analizó literatura en las bases SciELO y SCOPUS restringiendo términos de búsqueda a: seguridad alimentaria, COVID-19, tecnología de bloques o blockchain, suministro alimenticio, micronutrientes, regulación, fortificación con hierro con énfasis en productos a base de pulpa de café. Resultados: en tiempos de pandemia y otros desastres, uno de los factores que afectan la respuesta de un huésped al virus es la nutrición, la seguridad alimentaria es importante especialmente en países con altas tasas de desnutrición y anemia, por ende, es fundamental la fortificación de alimentos comunes para contribuir en garantizar la adecuación nutricional como parte de las respuestas de los gobiernos, especialmente en áreas rurales y urbanas empobrecidas, planteándose sistemas de suministro de alimentos con la tecnología de bloques o blockchain. Conclusión: la fortificación de productos alimenticios a base de pulpa de café y el suministro que aplique tecnología de bloques podría ser una estrategia de respuesta a las consecuencias de la pandemia.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.770
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.002
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.019
GPT teacher head0.268
Teacher spread0.249 · 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.

Study designBench or experimental
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
Published2022
Admission routes1
Has abstractyes

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