Content Type: Short Telegram posts, instructional cards, and emergency announcements. — Official Bulletins and RSS Feeds: Rospotrebnadzor (summaries on ticks and infection vectors), Rosselkhoznadzor (images and lists of quarantine pests), and regional Ministries of Natural Resources. each — International Monitoring Systems: EPPO (European and Mediterranean Plant Protection Organization) to track the spread of invasive species toward the Russian Federation’s borders, and FAO (Food and Agriculture Organization of the United Nations) for global summaries on locusts and other agricultural pests. — Scientific News Aggregators: EurekAlert! and ScienceDaily (Biology section) for instant access to press releases regarding newly described species or breakthroughs in entomology.

Content Type: Interactive bot identifier, website feedback form. — Open Taxonomic Datasets: BOLD Systems (DNA barcoding system) and labeled iNaturalist datasets. These are used as the «ground truth» standard for training the bot’s computer vision. — User-Generated Content (UGC): A proprietary database of photos uploaded by users via the bot. After expert validation, this data is fed back into the system to improve the accuracy of recognizing specific angles and lighting conditions.

Content Type: Long-form articles on the website, analytical special projects, infographics. — Global Biodiversity Aggregators (API): GBIF (Global Biodiversity Information Facility) and iNaturalist. These are used to export large datasets of observation coordinates for specific species (e.g., dragonflies or monarch butterflies) over the last 5–10 years to identify patterns and anomalies in their migration routes. — Peer-Reviewed Scientific Journals: Annual Review of Entomology, Journal of Biogeography, Ecological Entomology. These serve as sources for finding meta-analyses and fundamental explanations for why and how migratory shifts occur. — Climate Databases: Open archives from meteorological services (e.g., Copernicus Climate Change Service) are used to correlate temperature anomalies with changes in insect life cycles.

Content Type: Audio features with guests, video lectures with 3D visualization, documentary analyses. — Academic Publication Databases: PubMed, BioOne, eLibrary (for Russian-language content). These are used not so much for facts, but for finding authors of recent or landmark research whom the editorial team might invite as guests. — University and Institute Archives:The Department of Entomology at Moscow State University, ZIN RAS, and specialized biomimetics laboratories. These sources are used to find speakers and gain access to internal materials that have not yet been widely published. — Historical and Humanities Databases:JSTOR or art museum archives are used to prepare segments at the intersection of disciplines (e.g., «Insects in Painting» or «The History of Metamorphosis Discovery»).

Content Types: 3D models on the website, macro illustrations in longreads, historical reproductions. — 3D Scanning Repositories: MorphoSource Platform. Provides access to raw data from micro-CT scans of insects, which media designers can adapt into interactive web models (glTF/USDZ formats). — Digitized Museum Collections: Natural History Museum (London), Smithsonian Institution. Sources of high-detail photographs of type specimens (holotypes) for external verification. — Historical Archives: Biodiversity Heritage Library (BHL). Used for finding beautiful, public domain, vintage entomological illustrations and atlases from past centuries.

(Machine) API Parsers (GBIF, iNaturalist) → stream of raw coordinates and taxonomy. (Machine) NLP Scraping of Scientific Databases → extraction of annotations and metadata. (Machine) Telegram Bot → receiving photos and metadata from users.

(CV Model) Preliminary classification of photos from the bot (Confidence Score calculation). (NLP Model) Clustering of scientific articles by topic (migration, morphology, invasions). (Data Cleaning) Filtering duplicates and overtly erroneous geotags (e.g., a tropical species in the Arctic zone). (Aggregator) Grouping observations by region and season to identify trends.

(Entomology Expert) Review of questionable identifications from bot data (Confidence < 80%) and validation of anomalous migration points. (Editor/Analyst) Interpretation of aggregated data: why did the species shift its range? (climate, trade, landscape change). (Editor/Fact-Checker) Verification of NLP model conclusions against articles before writing a long-form piece. (Designer/Illustrator) Transforming raw data into understandable infographics and diagrams for articles.

(Human + Machine) Layout of a seasonal analytical longread on species migration, complete with infographics based on verified data. (Human) Writing the longread and recording a podcast based on established facts. (Machine) Auto-announcements in Telegram, and updating the bot’s knowledge base with new confirmed species.

Confidence Threshold: Over 90%: The bot provides the species name with the label «High probability.» 70–89%: The bot provides the family/genus level with the label «Clarification required.» Under 70%: The bot states: «Could not identify accurately. Photo sent to entomologist for moderation.» Mandatory Disclaimer: In every response: «AI may make errors. For accurate diagnosis of hazardous species, please consult a specialist.» Harm Prevention: If the bot recognizes an invasive or dangerous species, it will not provide instructions for «self-elimination using chemicals, ” but will instead provide a link to the official guide of the relevant agency. Feedback: The „This is incorrect“ button under the bot’s answer generates a dataset for model fine-tuning.

Source Filter: The parser indexes only peer-reviewed journals (Q1–Q3 via Scopus/WoS) or reputable preprint servers (bioRxiv). Entity Extraction (NER): The model must extract: Latin species name, Geographic region, Key discovery. «Red Flag» Rule: If the parser finds an article about the discovery of a new disease vector or an aggressive invasive species in the audience’s region, the material is automatically tagged «Urgent» and placed in the editor’s queue outside the general schedule.