Project Rigor Mortis: A Symposium of Systemic Toxicity

"From the Grain to the Glitch"

1. Abstract

"Exploration and Illumination"

This repository serves as a compendium of structural parallels between the "Generally Recognized as Safe" (GRAS) loophole in the US food supply and the "Terms of Service" (ToS) consent loopholes in Big Tech. It documents the "Slow Kill" paradigm—where latency is used to obfuscate liability.

Status Note: This project has transitioned from structural prototyping to active data ingestion. The Analysis Engine (src/correlation_mapper.py) currently processes raw federal datasets (CDC PLACES, USDA FARA) alongside live OSINT spatial telemetry to mathematically map the correlation between consumption, environment, and pathology. This is a living civic audit; data will be continuously refined to combat federal reporting latency and structural siloing.

2. The Modules (Research Vectors)

Phase I: The Biological Backdoor

• Focus: The "Cereal Killer" Logic (BHT, Chlormequat).
• The Loophole: GRAS self-regulation vs. Public Health.

Phase II: The Psychological Backdoor

• Focus: The API as a Surveillance Tool.
• The Mechanism: 3rd Party Data Brokerage & Algorithmic Addiction.

Phase III: The Sterility Vector

• Focus: The "Silent Castration" (Porcine/Human Analogs).
• The Data: Danish Pig Studies vs. Global Fertility Rates.

Phase IV: Mortality & Identity

• Focus: The Cost of "Programming" & Identity Fracture.
• The Metric: Trevor Project 2024 Data vs. High-Engagement Algorithms.

Phase V: The Pharma Integration

• Focus: The "Invisibility Shield" & Symptom Monetization.
• The Mechanism: 340B Loopholes, IRS Form 990 Charity Deficits, and the Psychiatric Pipeline.

3. The Tooling

Correlation Mapper

• Objective: Mapping "Dollar General" density vs. CDC Cancer/Morbidity Rates.
• Status: Static POI Density Mapping Active.

Data Latency Note: The federal datasets used in this engine highlight structural data siloing. The CDC health data is current to 2024, while the most recent USDA Food Access Research Atlas (FARA) is constrained to 2019. This latency makes cross-agency health auditing intentionally difficult. The correlation mapper bypasses this by normalizing the timeline delta via FIPS tract matching.

The "Slow Kill" Matrix (Visual Receipts)

A. Statistical Correlation Chart

How to Read This Data: This matrix visualizes the Top 100 most impoverished Census Tracts in the United States, cross-referencing federal CDC/USDA data with OpenStreetMap distributor density.

• The X-Axis (The Pipeline): Represents the number of "Discount Variety" stores within a 5km radius.
• The Y-Axis (The Outcome): Represents the prevalence of chronic health anomalies in that exact tract.
• The Conclusion: There is a positive association between discount store density and chronic health prevalence among high-poverty tracts. Further multivariate analysis is required to assess causality.

B. Geospatial Threat Map (Folium)

Click the map below to access the raw interactive HTML file (docs/vulnerability_map.html), or download it to view the exact coordinate overlays.

Threat Level Key:

• Yellow (0-1 Stores): Baseline poverty zone.
• Orange (2-4 Stores): Elevated exposure to GRAS loop-hole distributors.
• Crimson (5+ Stores): Critical Density. The tract is saturated by ultra-processed inventory.

4. The Evidence Locker (Receipts)

A chain-of-custody archive for primary source documentation.

Reports

• Kim (2023): Duke University Policy Lab report on the sale of mental health data.
• Facebook Files (2021): Internal "Teen Mental Health Deep Dive" deck.
• Trevor Project (2024): Suicidality and Identity metrics.

Toxicology

• Levine et al. (2017): Meta-analysis of Western sperm count decline (52.4% drop).
• The Paraquat Papers: Internal Syngenta memos regarding emetic additives and toxicity.

OSINT & External Datasets

Note on Raw Data: Due to GitHub's 100MB file size constraints, the raw .csv files used for local correlation mapping are excluded from this repository via .gitignore. For full transparency and civic auditing, you can pull the exact primary source datasets below into your local data/ directory.

• US Discount Food Store Footprint (2026): Consolidated OpenStreetMap extraction for Dollar General/Family Dollar coordinates. Uploaded to Kaggle for open-source civic auditing.
• CDC PLACES: Local Data for Better Health: Census tract-level health metrics (cdc_places_health.csv).
• USDA Food Access Research Atlas: Food desert and geographic accessibility metrics (usda_food_deserts.csv).
• FDA GRAS Substances (SCOGS) Database: Generally Recognized as Safe list for additive and chemical cross-referencing (fda_gras_list.csv).