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Enregistrement W2782865253 · doi:10.1002/mnfr.201870014

MNF's Upcoming Topics, Structure, and Standards in 2018

2018· editorial· en· W2782865253 sur OpenAlexaboutno aff
Hans‐Ulrich Humpf, Claus Schneider, Jan F. Stevens, Christine Mayer

Notice bibliographique

RevueMolecular Nutrition & Food Research · 2018
Typeeditorial
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueNutrition, Genetics, and Disease
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPublicationImpact factorChinaContext (archaeology)Library scienceGlobePolitical scienceMedicineGeographyComputer scienceLaw

Résumé

récupéré en direct d'OpenAlex

Molecular Nutrition & Food Research (MNF) is well beyond its infancy. Since its relaunch in 2004, the journal has grown continuously in the number of submissions and published manuscripts. With a current ISI Impact factor of 4.323 (>4 for the 8th year in a row) and a 5-year impact factor of 4.518, MNF has established itself as one of the leading journals in the field of molecular nutrition research. This is largely an accomplishment of our authors—we can publish a high-ranking journal only if we receive innovative, high-impact manuscripts. In this context, we would like to draw your attention to the three most highly cited articles from 2016, which focus on some of the current top research subjects in the field of nutrition: gut microbiome,1 inflammation & obesity2 and metabolism.3 The journal's contributions originate from a truly international author landscape: 5 continents and 43 countries, with USA, China, Spain, Germany, and Japan comprising more than half of all published papers. Our readers (>330000 full-text downloads in 2016) are also spread across the globe: USA, China, Japan, South Korea, Germany, UK, Italy, Spain, Canada, and Australia constitute the top 10. With the first issue of MNF in 2018, we will again present our annual reviews issue with the addition of selected meta-analysis articles. Year after year, this is the most downloaded and cited issue of the journal. The topics in the current collection range from safety aspects in novel foods and allergy to epigenetics as well as berry-based food interventions and infant nutrition. Later in the year, we will publish two topical special issues. The first special issue will cover various aspects of isothiocyanates and health, guest edited by Emily Ho (Oregon State University) and Richard Mithen (Norwich Research Park). It will summarize current findings in the areas of bioavailability in foods and crops, glucosinolate hydrolysis, epidemiology, clinical trials, and more. The second special issue is edited by Lorraine Brennan (University College Dublin) and will cover two related and timely topics, namely metabolomics and biomarkers. This reflects the increasing impact that these emerging fields have in nutrition research. As nutrition science is continually evolving so are the demands of rigorous reporting of its results; thus, manuscript guidelines and formats, nomenclature, database deposits, etc., need periodic revision. In 2017, we have added new, specific details on allergen nomenclature rules in accordance with the WHO/IUIS Allergen Nomenclature initiative (see author guidelines). In 2018, we will ask our authors to clearly define doses of applied active compounds (including the human equivalent dose (HED)) for in vivo experiments and clinical trials in the materials and methods section. On an editorial note, MNF will be moving to an online-only format with publication frequency increased to 24 issues per year. This reflects the growing number of accepted papers and ensures faster turnover. As our regular readers will have noticed, we have changed the format of the articles slightly and have replaced page numbers with e-locators. This enables instant full citation of articles without the need to wait for issue publication. As announced already in last year's editorial and implemented as of 2017, the continuously increasing number of submitted manuscripts has led to the decision to change the peer-review process for the journal. Reviewer selection and invitation, as well as manuscript evaluation and decision, are now handled by an in-house team of editors who will dedicate their full attention to this task. In continuation of this change, we will split the main responsibility of leading the journal into two roles: The external academic lead as Chair of the Executive Editorial Board (Hans-Ulrich Humpf) and the in-house Editor-in-Chief (Chris(tine) Mayer). The in-house team of editors handling the peer review (Chris Mayer, Kerstin Brachhold, Ana V. Jobling Almeida, Xie Cai) will work together with the slightly restructured Editorial Board (the Associate Editors and Senior Editors will form the new Executive Editorial Board) to ensure that MNF continues to publish at the forefront of molecular nutrition research. “As a personal note, I would like to thank Hans-Ulrich Humpf for his enthusiasm and dedication to the journal in his role as Editor-in-Chief. Having held this position since 2013, he has been responsible for the high-quality course the journal has continued on since taking over from Peter Schreier. However, this is thankfully not a goodbye but rather a slight shift in task distribution, as he will stay on as the academic lead as Chair of the Executive Editorial Board. A second thank you goes to Claus Schneider and Fred Stevens for their excellent work as Associate Editors. I am pleased to say that they will also remain in key roles on the newly formed Executive Editorial Board” – Chris Mayer

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,041
score de la tête « metaresearch » (Gemma)0,103
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: Éditorial
Score de désaccord entre enseignants0,070
Score d'incertitude au seuil0,233

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0410,103
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0030,003
Bibliométrie0,0130,006
Études des sciences et des technologies0,0050,003
Communication savante0,0250,014
Science ouverte0,0060,008
Intégrité de la recherche0,0130,010
Charge utile insuffisante (le modèle a refusé de juger)0,0700,083

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,017
Tête enseignante GPT0,346
Écart entre enseignants0,329 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreÉditorial

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2018
Routes d'admission1
Résumé présentoui

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