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Record W2903368942

TRATAMIENTOS POSCOSECHA CON MICROONDAS DE JITOMATE PARA EL CONTROL DE MICROORGANISMOS

2018· article· es· W2903368942 on OpenAlexaboutno aff
Fatima Guadalupe Hernández González, María Elena Sosa Morales

Bibliographic record

Venuenot available
Typearticle
Languagees
FieldAgricultural and Biological Sciences
TopicPlant and soil sciences
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPhysicsArt
DOInot available

Abstract

fetched live from OpenAlex

El jitomate es una hortaliza ampliamente cultivada y consumida en Mexico y es exportada principalmente a Estados Unidos, Canada y Japon. El jitomate es atacado por algunos microorganismos, entre ellos, el moho Botrytis cinerea causando perdidas de hasta 30% de la cosecha. Se han buscado alternativas que disminuyan la perdida por el moho que y conserven sus propiedades fisicoquimicas de los mismos sin hacer uso de pesticidas. El objetivo del presente estudio es proponer un tratamiento con microondas alcanzando una temperatura objetivo para la muerte del moho, sin danar las propiedades fisicoquimicas del alimento. Se usaron lotes de 350±10g de jitomates Saladette inmaduros sumergidos en 330 g de agua, se calentaron con microondas a 206 o 502 W de potencia en el horno hasta alcanzar una temperatura interna de 48°C por 1.24 min (temperatura y tiempo de muerte de B. cinerea) y se enfriaron con agua fria a 10°C. Se dejo un lote sin tratar como testigo. Los jitomates se almacenaron a temperatura ambiente durante 13 dias, analizando muestras los dias 1, 5, 9 y 13. El tratamiento a 502 W logro mantener las propiedades fisicoquimicas del jitomate por mayor tiempo en comparacion al tratamiento a 206 W y el control, por lo que el tratamiento a 502 W previene el deterioro del jitomate causado por Botrytis cinerea durante 13 dias a temperatura ambiente.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.245
Teacher spread0.231 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations0
Published2018
Admission routes1
Has abstractyes

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