Journal Finder, part of Crimson AI’s Trinka toolset (by Enago), recommends journals from a manuscript’s title, abstract or keywords. It uses NLP concept-matching against a database of millions of published papers drawn from sources such as OpenAlex, Crossref…
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MDPI’s Journal Finder suggests journals from a paper’s title and abstract, using an NLP model that builds a numerical representation of the text for similarity search against each journal’s published content.
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Springer Nature’s Journal Finder recommends journals from manuscript content, embedded directly in the submission workflow.
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Journal Fit is a feature in Hum’s Alchemist Review that scores how well a submitted manuscript aligns with a journal’s scope and standards: an at-a-glance Fit Score (0–10) calculated across four dimensions — Novelty, Relevance to Scope, Scientific…
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Springer Nature’s Cureus launched the Journal of AI-Augmented Research as “an open and trusted home for researchers using AI as part of their research practice, with clear standards for transparency, reproducibility, and human-verified validation.”Notes & quotesIt was only…
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Molecular Connections built a Journal Recommendation Service for IFIS, the food and health sciences publisher. It analyses abstracts and matches them to journal scopes across the FSTA database of trusted journals — over 1,000 titles, food-focused and interdisciplinary.
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JSTOR’s interactive research tool, in beta, adds custom summaries, recommended topics, related content and document Q&A to the platform — users can “talk to the document”. Runs on GPT-3.5 plus smaller open-source models for semantic search.
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Seeklight is JSTOR’s AI tool for archivists — generating high-quality metadata through a multi-step process of classification, prompts and human oversight. Co-developed with practitioners as part of JSTOR’s digital stewardship work, and purpose-built to support archivists rather than…
Kairos, from Every Studio, is a reading companion born of a workaround: its creator kept toggling between an ebook and ChatGPT, copying passages to ask questions, and decided the workaround was the product. Ask questions, get clarifications and…
Molecular Connections built a high-throughput Keyword Recommendation Service for IFIS, the not-for-profit food and health sciences publisher. Deep learning models — semantic similarity, contextual vectorisation, bi-directional transformers — analyse the Food Science and Technology Abstracts database and suggest…
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