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Principios, aplicaciones y efectos de la aplicación de plasma frío en alimentos: una revisión actualizada

2024· article· es· W4396980990 on OpenAlex
Luís Puente

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

VenueRevista chilena de nutrición · 2024
Typearticle
Languagees
FieldAgricultural and Biological Sciences
TopicMeat and Animal Product Quality
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

Resumen En el ámbito de las tecnologías no térmicas para el procesamiento de alimentos, la aplicación de plasma frío destaca por su rápido crecimiento y amplias proyecciones. El plasma frío se genera aplicando energía que ioniza un gas específico, lo que produce especies altamente reactivas como las reactivas de oxígeno y nitrógeno, además de ozono, iones, radicales libres y radiación ultravioleta. Las configuraciones más comunes para su generación incluyen la descarga de barrera dieléctrica y la descarga corona. Sus aplicaciones y efectos clave abarcan la destrucción de biofilms, inactivación de microorganismos, descontaminación de micotoxinas, degradación de pesticidas y modificación de almidones, entre otros. Los mecanismos de acción propuestos varían desde modificaciones químicas y moleculares hasta lisis celular y daño genético. Este artículo proporciona una visión general actualizada sobre los principios, generación y aplicaciones del plasma frío en la industria alimentaria.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.914
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0020.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.014
GPT teacher head0.292
Teacher spread0.278 · 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