Ingesta de Nutrientes: Conceptos y Recomendaciones Internacionales (2ª Parte) Nutrient intakes: concepts and international recommendations (part two)
Bibliographic record
Abstract
Objetivo: Esta revisión sobre la ingesta de nutrientes pretende analizar, comparar y evaluar los distintos conceptos y datos utilizados por diferentes organismos y autoridades nacionales e internacionales y reflejar su plasmación legislativa y su evolución en el tiempo. Al mismo tiempo facilita el acceso bibliográfico y por Internet a dichas fuentes y al final ofrece un glosario de términos y sus acrónimos. Ámbito: Se han considerado 4 espacios geográficos, estructurados en 2 partes. Primera parte: Unión Europea. Segunda parte: España, Estados Unidos de América/Canadá y FAO/OMS. Debido a la extensión del texto de esta revisión ha sido necesario dividirla en 2 partes, publicadas en números consecutivos de la revista Nutrición Hospitalaria. Los datos analizados se refieren exclusivamente a las personas sanas. Conclusiones 2&ordf; y 1&ordf; partes: En España se han registrado avances relevantes en materia de encuestas alimentarias y tablas de composición de alimentos. A nivel internacional,se ha producido un refinamiento y ampliación de los conceptos utilizados y un desglose progresivo de los datos por grupos de población, especialmente en más de 50 años, embarazo y lactancia, aunque se evidencian importantes disparidades entre los diversos organismos y autoridades.<br>Objective: This revision on nutrient intakes pretends to analyse, compare and evaluate the various concepts and data used by different national and international bodies and authorities, reflecting their turn into to legal norms and their evolution in recent years. At the same time it facilitates bibliographic references and Internet websites to those sources and at the end it offers a glossary of terms and their acronyms. Scope: Four geographical territories have been considered, being split in 2 parts. First part: European Union.Second part: Spain, United States of America/Canada and FAO/WHO. Due to the extensive text of this revision there has been necessary to divide it in 2 parts which are being published in consecutive numbers of the journal Nutrición Hospitalaria. Conclusions of the 2nd (and 1st) part: Important advances in food consumption surveys and as well as in the tables of food composition, have been published in Spain.At the international level the concepts used have been refined and broadened with an ever increasing breakdown by population groups, especially for those 51 and over, and pregnant and/or nursing women. However, there exist many disparities among the diverse authorities and organizations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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 teacher head, 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".