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Overview

The Subnet 42 scoring system evaluates miner performance by analyzing telemetry data collected from their TEE (Trusted Execution Environment) workers. This scoring mechanism is designed to reward miners that successfully process data collection tasks.

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How telemetry data is collected and processed

Telemetry Data Sources: Each registered TEE worker periodically reports telemetry data that includes:

Twitter Metrics

✓ Tweet collection statistics
✓ Profile data retrieval metrics
✓ API usage and rate limit tracking

Web Metrics

✓ Success and failure counts for web scraping operations
✓ Performance tracking across different target sites

TikTok Metrics

✓ Success and failure counts for web scraping operations
✓ Performance tracking across different target sites

Reddit Metrics

✓ Success and failure counts for web scraping operations
✓ Performance tracking across different target sites

Error Monitoring

✓ Authentication failures
✓ Rate limit exceeded events
✓ Network and connectivity issues
✓ Other operational errors

Timing Data

✓ Operation start/end timestamps
✓ Processing duration metrics
✓ Interval between data collections

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Scoring Algorithm

1

Telemetry Data Collection

Analyzes changes in miner performance metrics over time using delta-based calculations
2

Extract & Normalize

Standardizes raw metrics into comparable values across different data types
3

Apply Weighting

Uses source-based weighting to reward miners based on the demand they fulfill
4

Calculate Scores

Combines weighted metrics into comprehensive performance scores
5

Kurtosis Weighting

A custom kurtosis function weights top performers more heavily in the final scoring calculations.
6

Generate Weights

Converts final scores into network weight allocations for rewards

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Conclusion

For detailed information about the specific weights and scoring parameters each data source, check the configuration file, which contains the exact weight values used by the subnet validators.