Características reologicas y texturales de pasta de tomate (Solanum lycopersicum L.) comercializada en la ciudad de trujillo - 2019.
Bibliographic record
Abstract
El objetivo de esta investigación fue determinar las características reológicas y texturales de \ntres marcas diferentes de pastas de tomate comercializadas en la ciudad de Trujillo. El diseño \nestadístico correspondió a uno de un factor (marca comercial de pasta de tomate), con 3 \nréplicas. Primeramente se evaluó el cumplimiento de los supuestos de normalidad y \nhomogeneidad de varianzas , mediante las pruebas de Anderson-Darling y Levene \nmodificada, respectivamente; al cumplirse estos, se realizaron las pruebas paramétricas del \nanálisis de varianza (ANOVA), y a continuación, al existir diferencias significativas (p<0.05) \nse aplicó la prueba de comparaciones múltiples de Tukey la cual comparó los resultados \nmediante la formación de subgrupos y se determinó de esta manera el mejor tratamiento. \nTodas las pruebas estadísticas se realizaron con un nivel de confianza del 95%. Para procesar \nlos datos se utilizó los softwares R 3.2.5 y R 3.4.1, con los paquetes "agricolae" y "car", \nrespectivamente.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".