> ## Documentation Index
> Fetch the complete documentation index at: https://developers.gopher-ai.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Scoring

## 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.

### How telemetry data is collected and processed

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

<CardGroup cols={2}>
  <Card title="Twitter Metrics" icon="twitter">
    ✓ Tweet collection statistics\
    ✓ Profile data retrieval metrics\
    ✓ API usage and rate limit tracking
  </Card>

  <Card title="Web Metrics" icon="globe">
    ✓ Success and failure counts for web scraping operations\
    ✓ Performance tracking across different target sites
  </Card>

  <Card title="TikTok Metrics" icon="tiktok">
    ✓ Success and failure counts for web scraping operations\
    ✓ Performance tracking across different target sites
  </Card>

  <Card title="Reddit Metrics" icon="reddit">
    ✓ Success and failure counts for web scraping operations\
    ✓ Performance tracking across different target sites
  </Card>

  <Card title="Error Monitoring" icon="triangle-exclamation">
    ✓ Authentication failures\
    ✓ Rate limit exceeded events\
    ✓ Network and connectivity issues\
    ✓ Other operational errors
  </Card>

  <Card title="Timing Data" icon="clock">
    ✓ Operation start/end timestamps\
    ✓ Processing duration metrics\
    ✓ Interval between data collections
  </Card>
</CardGroup>

### Scoring Algorithm

<Steps>
  <Step title="Telemetry Data Collection">
    Analyzes changes in miner performance metrics over time using delta-based calculations
  </Step>

  <Step title="Extract & Normalize">
    Standardizes raw metrics into comparable values across different data types
  </Step>

  <Step title="Apply Weighting">
    Uses source-based weighting to reward miners based on the demand they fulfill
  </Step>

  <Step title="Calculate Scores">
    Combines weighted metrics into comprehensive performance scores
  </Step>

  <Step title="Kurtosis Weighting">
    A custom kurtosis function weights top performers more heavily in the final scoring calculations.
  </Step>

  <Step title="Generate Weights">
    Converts final scores into network weight allocations for rewards
  </Step>
</Steps>

## Conclusion

For detailed information about the specific weights and scoring parameters each data source, check the [configuration file](https://github.com/gopher-lab/subnet-42/blob/main/validator/platform_config.py), which contains the exact weight values used by the subnet validators.