Aspectos destacados del VII congreso internacional de nutrición vegetariana
Bibliographic record
Abstract
El VII Congreso Internacional de Nutrición Vegetariana (CINV) se realizó del 26 al 28 de febrero del 2018 en Loma Linda, CA. Este evento se realiza cada 5 años y convoca a los principales expertos de la dieta basada en plantas para exponer al personal del área de la salud y personas interesadas, las más recientes investigaciones que se han realizado en este aspecto de la nutrición1. Los ponentes fueron reconocidos médicos, investigadores y expertos en nutrición de distintas instituciones médicas y educativas alrededor del mundo como la Universidad de Harvard, la Universidad de Loma Linda, la Universidad de Yale, la Universidad de Oxford, la Universidad de Otago, la Universidad de Toronto, entre otras. El tema del VII CINV de este año fue “Nutrición basada en plantas para la salud personal, de la población y del planeta”. Bajo este enfoque se expusieron los beneficios de seguir esta alimentación para prevenir, controlar e incluso revertir algunas de las enfermedades no transmisibles como la diabetes mellitus, la hipertensión arterial, la obesidad y el cáncer. DOI: 10.25176/RFMH.v18.n2.1293
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".