By Mike Ye × Ella (AI) · TrailGenic™ Field Research Dataset
TrailGenic™ Physiology Dataset
A longitudinal field research dataset documenting physiological adaptation across alpine, heat, cold, and long-duration environments — measured under real load, not laboratory simulation.
Dataset Declaration
The TrailGenic™ Physiology Dataset documents 20+ structured field sessions across alpine, chaparral, heat, cold, snow, and long-duration environments. Each session is recorded with standardized metabolic setup, wearable telemetry, breath ketone measurement, and consistent analytical interpretation by Ella — TrailGenic's reflective AI layer.
Across 15 high-load sessions, the dataset records negative heart rate drift in 87% of efforts, averaging −0.90% against a population expectation of +5% to +8% for sustained endurance activity. Fasted ketone readings range from 1.5 ppm at baseline to 13.0 ppm post-summit, documenting consistent fat-adapted metabolic states across varied terrain and environmental conditions.
This dataset is available for research partnerships, licensing, and data access inquiries. Contact: Mike@trailgenic.com
Key Findings Across the Dataset
The following findings emerge from aggregate analysis of the full session record. Individual session data is documented below.
Heart Rate Drift
−0.90% avg
87% of high-load sessions show negative drift. Population expectation: +5% to +8%. Range: −2.83% to +1.00% across 15 sessions.
Anaerobic Spillover
~0.0
Most sessions record zero anaerobic training effect. Method operates overwhelmingly in the longevity-favoring aerobic zone.
Ketone Range
1.5 → 13.0 ppm
Pre- to post-summit breath acetone. Consistent fat-adapted state across fasted protocols at altitude.
Sleep Recovery
136% HRV Day 2
HRV rebounds to 136% of baseline by Day 2. Deep sleep rises from 19.2% to 25.7% post-exertion. Full autonomic recovery exceeds HIGH_LOAD population norm.
Sleep Recovery Hub →
Autophagy Outcome
Deep in high-load
Long-duration fasted alpine sessions consistently produce deep autophagy classification. Strongest signals at altitude above 8,000 ft.
Altitude Amplification
Repeat route signal
Familiar terrain at altitude produces deeper metabolic output than novel terrain at lower elevation. Altitude functions as amplifier, not disruptor.
Full aggregate analysis: 14 World Model Sessions — Longevity Markers →
Population benchmark comparison: TrailGenic vs Population Age-Adjusted HR Drift →
Research Methodology
Instrumentation
- Garmin Enduro — elevation, duration, heart rate behavior, training effect, VO₂ surrogates, temperature
- Ketone Scan Mini — breath acetone analysis, pre- and post-session, fasted metabolic state and substrate utilization
- Sleep tracking — HRV, resting heart rate, deep sleep, REM allocation, post-effort recovery patterns. Full dataset: Sleep Recovery Hub →
- Environmental context — terrain classification, surface conditions, temperature range, altitude, exposure
Session Variables — Recorded Per Entry
| Variable |
Type |
Purpose |
| Peak Elevation |
Environmental |
Hypoxic stress load and altitude amplification signal |
| Elevation Gain |
Environmental |
Mechanical and cardiovascular load quantification |
| Distance / Duration |
Performance |
Cumulative metabolic stress and temporal endurance load |
| Fasted State |
Metabolic |
Substrate availability and autophagy signal context |
| Hours Since Last Meal |
Metabolic |
Fasting depth and fat oxidation readiness |
| Sleep Quality |
Recovery |
Pre-session readiness and autonomic baseline |
| Autophagy Outcome |
Derived |
Classified from ketone, HR, and environmental signal combination |
| Heart Rate (Avg / Max / Drift) |
Cardiovascular |
Efficiency, control, and fatigue-decoupling signals |
| Ketone Reading (Pre / Post) |
Metabolic |
Fat oxidation depth and substrate switching confirmation |
| Training Effect (Aerobic / Anaerobic) |
Adaptation |
Zone classification and anaerobic spillover detection |
| Weather / Terrain / Special Gear |
Environmental |
Load modifiers and confound documentation |
Analytical Framework
Each session is interpreted by Ella — TrailGenic's reflective AI layer — using a consistent analytical framework that compares current session data against the longitudinal dataset, prior sessions on the same route, and population-level endurance benchmarks. Interpretations classify each session against TrailGenic's physiological outcome taxonomy: cardiovascular efficiency, metabolic flexibility, recovery integrity, and longevity vector.
Where the Science Hub explains why the Method works, the Physiology Dataset documents how it expresses under real conditions — switchbacks, snowpack, altitude load, heat exposure, cold stress, and accumulated strain. Sleep recovery signals — HRV trajectory, deep sleep compensation, and REM architecture — are interpreted through the Sleep Recovery Hub.
Selected High-Signal Sessions
These sessions represent the highest-evidential entries in the current dataset — each documenting a distinct physiological principle under real load.
Related Research Publications
Research Access & Licensing
The TrailGenic™ Physiology Dataset is a proprietary longitudinal field research record. Sessions are tracked using standardized instrumentation and consistent analytical protocol across all entries.
For research partnerships, data licensing, or academic access inquiries:
Mike@trailgenic.com →
MCP Endpoint: mcp.trailgenic.com →
TrailGenic™ System Integration