Wire TriggerExplainer into step_explain so explanation generation
can be switched from the default free template path to a Claude-
Haiku-4.5 path via EXPLANATION_MODE=ai. Default stays "template"
so nothing changes at runtime without an opt-in.
- config.py: read EXPLANATION_MODE env var (default "template")
- src/explanation/explainer.py: switch TriggerExplainer default
model from Sonnet to Haiku 4.5 (advisory rewriting is a
template-fill task, not reasoning)
- src/pipeline.py: dispatch Template vs Trigger in _step_explain
based on the flag; report mode and cost in StepResult details
- frontend/src/pages/Pipeline.tsx: add ScalingCostPanel showing
three tiers (current/pilot/state-wide) on the Architecture tab
The design point the panel makes: LLM cost is decoupled from
worker population because explanations fire at trigger events
(a handful per week), not per worker. Pilot at 106K workers
costs the same ~$0.05/wk as the demo; AI explanations on add
less than a dollar even at 1…
Closes the AI/ML sophistication gap with Weather AI 2:
- ERA5 real climate data via Google ARCO archive (free, no API key)
- PyTorch LSTM heat predictor ensembled with XGBoost (4-tier degradation)
- FAISS+BM25 hybrid RAG for alert generation (44-doc index)
- Neon PostgreSQL schema (11 heat-specific tables) with asyncpg + InMemory fallback
- 19-test eval suite (UHI accuracy, predictor AUROC, RAG precision, healing recall)
- Fix broken flood-era pipeline/store/schema imports
- De-jargonify all frontend text for donor/program officer audience
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
6-step ML pipeline for parametric heat insurance in East Africa.
Three XGBoost models (UHI correction, heat wave prediction, actuarial pricing),
Claude AI healing agent, interactive program designer with budget allocation.
20 urban zones across Nairobi, Dar es Salaam, Kampala, Kigali.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
