Permafrost Automation Archi

Full-Stack System Architecture Model, 7-Step AI Documentation Pipeline, and Alternative IDE / Model Landscape demonstrating end-to-end field automation for the Permafrost School research project.

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Page 1: Full-Stack Model

5 color-coded architectural layers: Source Inputs, LLM Processing Engine, Next.js Web App, CI/CD Pipeline, and 5-Nation Audience.

Page 2: 7-Step Pipeline

Step-by-step transformation from Russian WhatsApp field messages and Sakha phrases to published academic English in <6 hours.

Page 3: Alternative Tools

Complete stack comparison: Antigravity vs Cursor vs VS Code; Next.js vs Nuxt/Astro; JSON flat-file vs Supabase/Sanity CMS.

Page 4: AI Model Matrix

Gemini 2.5 Pro vs GPT-4o vs Claude 3.7 vs Llama 3.3 evaluated for code generation, multilingual capability, and agentic workflows.

Dodiya_Project_Architecture_v1.html (4 Pages Ready for Demonstration)

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Global Telemetry Evidence

764 Total Pageviews within 13 days of live fieldwork
73.3% International Readership outside the Russian Federation
Peak 212 Views/Day on July 19 (90-yr Met. Station expedition)
5 Nations: India (44.8%), Russia (26.7%), USA (8.9%), Kazakhstan (5.0%), Others (14.7%)

Pipeline Speed & Automation

< 6 Hours publication latency from field WhatsApp to live web
< 3 Minutes Vercel CI/CD auto-deploy to global CDN on git push
Zero Translation Lag vs. months in traditional academia
Bilingual Toggle via React useState() with zero page reloads

Sociolinguistic Innovation

Trilingual Complementarity: Russian, Sakha, and English
Cultural Preservation: Sakha tokens authenticated, not erased
SPbU Conference Paper: Section 10 submission ready
Replicable Blueprint for Arctic STEM initiatives globally