The Macroscope Collaboratory

@techreport{hamilton2026macroscope, author = {Hamilton, Michael P., Ph.D.}, title = {The Macroscope Collaboratory}, institution = {Canemah Nature Laboratory}, year = {2026}, number = {CNL-TN-2026-047}, month = {april}, url = {https://canemah.org/archive/document.php?id=CNL-TN-2026-047}, abstract = {The Macroscope Collaboratory is a structured scientific investigation platform that guides human-AI collaborative research through a seven-phase workflow: Seed, Priors, Proposal, Workflow, Testing, Conclusions, and Reflections. Built as part of STRATA 2.0, the Collaboratory integrates MNG's ecological address system with real-time sensor data to provide grounded, place-based context for AI-assisted environmental investigations. This technical note documents the architecture and first complete operational test of the Investigation Wizard -- the interactive interface where an investigator and an LLM work through each phase collaboratively. The system assembles a structured system prompt from ecological context priors (geology, climate, ecoregion, land cover, biodiversity), selected instruments (live sensor platforms from the macroscope database), and a persistent lab notebook that serves as the investigation's memory across phases. First-light testing on April 9, 2026 exercised the complete seven-phase pipeline from Seed through Reflections: investigation creation with site selection, automated ecological context extraction from seven lookup\_cache sources plus iNaturalist species data, context injection into the LLM system prompt, and full wizard execution against live Tempest weather station data. The test investigation (STR-001) analyzed diurnal temperature range consistency over a 7-day window, producing 42 notebook entries across all seven phases at a total cost of \$0.94 using primarily Haiku 4.5. The ecological context priors successfully grounded the AI's reasoning in validated place data -- referencing the correct Csb climate classification, NLCD land cover, geological substrate, and 35-year climate normals without hallucination. The Testing phase demonstrated the critical role of investigator oversight: Dr. Hamilton identified a timestamp anomaly (daily maxima reported at 23:xx instead of the expected 4 PM), a partial-day artifact inflating variability metrics, and a mischaracterized statistical test. The AI corrected its analysis in response, revising the coefficient of variation from 29.7\% ("highly variable") to 17.1\% ("moderately consistent") -- a materially different conclusion that validates the seven-phase workflow's built-in error correction mechanism. Several architectural bugs were identified and resolved during testing, including model selection persistence from admin to wizard, phase name consistency in the system prompt, NLCD data extraction format mismatch, and mysqli protocol errors.} }