Bibliographic record
Abstract
Molecular Microbiology is seeking original research and review articles for a special section that will focus on the One Health initiative and the Sustainable Development Goals of the United Nations. These initiatives are geared toward increasing scientific knowledge and developing molecular tools to monitor and control microbial activities with an emphasis on helping the global community tackle infectious diseases, promote sustainable agriculture, and protect the environment. To support research communities working at the intersection of microbiology, health, climate, and sustainable research, Molecular Microbiology is introducing a new section called “One Health Microbiology.” This section will cover a broad range of disciplines, including infectious diseases, molecular biology, environmental sciences, biotechnology, immunology, pharmaceutical sciences, food sciences, social sciences, economics, and multidisciplinary sciences. The aim is to provide a holistic view of the field and offer high-quality, interdisciplinary content that meets the highest standards. The journal is now calling for contributions to create an authoritative collection of articles that will present research, concepts, and challenges related to One Health initiatives. The articles should be of interest to a wide audience, address challenging scientific questions, offer biological or mechanistic insights, propose solutions to existing problems, or provide a perspective on the future of the field. While the journal encourages submissions of original research or review articles, all types of articles will be considered. The initial deadline for submission is July 15, 2023, with a secondary target submission date of September 30, 2023. To submit a manuscript to Molecular Microbiology's “One Health Microbiology” section, authors must follow the Author Guidelines and use the journal's online submission system. During the submission process, authors are asked to clearly indicate that their contribution is intended for this section. All submitted manuscripts will be peer-reviewed according to the journal's publishing policies. Accepted articles will be published online without charge. While authors are not required to pay Article Processing Charges (APCs), they have the option to publish their article as open access. If they choose this option, Wiley may cover the APCs through agreements with institutions and funders globally. Section Editors: Neil Gow, University of Exeter, Exeter, United Kingdom Leah Cowen, University of Toronto, Canada Monica Gandhi, University California San Francisco, United States Nicole De Nisco, University Texas Dallas, United States Jacomine Krijnse Locker, Paul Ehrlich Institut, Germany Friedrich Frischknecht, Heidelberg University, Germany Junior Editors: Daniel Fernando Rojas Tapias, Agrosavia, Mosquera, Colombia Michael Neugent, University Texas Dallas, United States
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.076 | 0.082 |
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".