Concept
«Paleo-Editorial» is a media platform that transforms the study of dinosaurs into a vibrant dialogue. Users don’t just read encyclopedic facts; they «flip through» sections. The main focus isn’t on dry species lists, but on how scientists discovered what they know and why that knowledge matters.
Media Goal: To popularize paleontology. The goal is to make complex science accessible and «warm"—free from academic dryness. AI functions here as the author, editor, and illustrator: it generates texts, answers questions, and draws analogies from modern life. Target Audience: People looking to expand their knowledge of paleontology. This includes teens seeking inspiration and contemplating future careers; adults with a nostalgic fondness for their childhood love of dinosaurs who are looking for high-quality intellectual leisure; and parents who want to spend time with their children in an engaging and educational way while exploring the topic together. Platform and Content: A Telegram bot featuring topic-based buttons. Additionally, there is a separate TG channel for announcements. Unique Selling Proposition (USP): A personalized dinosaur journal. Choose your topics, ask questions—and the AI editor responds in simple language, using relatable analogies tailored to your level.
Segment:
EdutainmentNiche Topic:
Dinosaurs and the Mesozoic Era. The text is written in simple, accessible language, offering clear explanations of scientific material. The focus is not on encyclopedic lists, but rather on how scientists discover what they know and why it is interesting.Data Sources for Media
For media to be diverse and timely, the system must integrate multiple types of sources. Priority is given to platforms with open APIs, RSS feeds, or databases.
Principles for Source Selection:1) Thematic Relevance — publications on paleontology, the Mesozoic Era, evolution, and scientific methodologies. 2) Reliability — presence of authorship, references to research, or validation from scientific organizations. 3) Automation Potential — availability of an API, RSS feed, or structured databases. 4) Diversity — a blend of dense scientific articles, news updates, and visual content.
Source Types:1) Scholarly Journals and Electronic Libraries (e.g., Paleobiology Database, eLife, Nature, Science). Searching for new peer-reviewed work.2) Popular Science Media (N+1, «Elements», SciTechDaily). Tracking current topics and breaking news.3) Museums and Educational Platforms (Smithsonian, Natural History Museum). Sourcing fact-checking information and high-quality reconstructions.4) Social Networks and Communities (Reddit r/Paleontology, specialized forums). Finding memes, user-submitted findings, and discussions.5) Open Media Libraries (Wikimedia Commons, paleontological artist portfolios). Curating visual assets for features.
Data schema and processing separation
The process is built on the principle of: automated data collection and initial processing, followed by editorial review.
Data CollectionMachine Monitoring selected sources (RSS/API) every 3–6 hours for news and once daily for scientific databases. Extracting titles, text/abstracts, author, source, date, and link. Assigning a unique ID to the entry and recording the retrieval time. Maintaining a log of requests and errors.Filtering and StructuringMachine Verifying link integrity and required fields. Duplicate searching (comparing URLs, DOIs, titles). Categorizing (science, species, news, visual materials). Assigning tags and routing questionable entries to a manual review queue.DraftingHuman The editor selects the curated material and generates a task for the AI (or the AI suggests a draft post based on the data). Sourcing available images and adding a link to the original source.Editorial ReviewHuman Fact-checking, verifying relevance, and quality assessment. Editing the AI-generated text to align with the media’s style. Coordinating illustrations and formulating the final headline.Scheduling and PublishingHuman + Machine The human approves the schedule and materials. The machine automatically posts content in the Telegram bot/channel at the designated time.Statistics CollectionMachine Collecting metrics such as views, reactions, and shares. Linking metrics to the publication ID. Measuring performance after 24 hours, 72 hours, and 7 days post-release.Results EvaluationHuman Analyzing publication effectiveness and adjusting the content strategy (which topics perform best).
Guidelines for Automated Services
Key rule: the machine performs only those operations for which clear processing rules and verifiable results are defined. Any situation requiring the interpretation of scientific facts or editorial judgment remains under human control.
General Guidelines for the System:Accuracy: Do not invent facts, quotes, sources, or scientific data (AI must not «hallucinate»).Transparency: Maintain links to original sources and note unverified information.Copyright: Do not copy others' materials without permission or an appropriate license.Security: Do not provide passwords, access tokens, or confidential data to third-party services.Editorial Oversight: Do not publish content in the Telegram bot without final approval from a human editor.Error Handling: In case of a failure, dubious result, or insufficient data, pass the task to an editor instead of fabricating an answer yourself.
AI Blocklist (What the machine MUST NOT do):During collection: Connect to unapproved sources, bypass authorization, alter original facts, or confuse the publication date with the discovery date.During filtering: Delete semantic duplicates without preserving the relationships between them, exclude materials solely because they lack a key word in the title, or substitute scholarly review with thematic classification.During statistics collection: Replace missing metrics with approximations, compare publications of different ages without considering the time elapsed since release, equate views to unique readership, or unilaterally change the editorial strategy based on statistics.
