# Sybil Report on Gitcoin Grants Rounds 13

###### tags: `gitcoin` `Reports`

:::info
Updated by March 2022
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*Authors: Danilo Lessa Bernardineli*

## Summary

An total of 11.9% of the Gitcoin users, representing `TODO`% of the contributions, were flagged during R13. The Sybil Incidence during this round is significantly lower than R12, with an estimate of being 70% of it was before.

The Flagging Efficiency was 84% (lower boundary: 77% and upper boundary: 93%) which means that the combined process is underflagging sybils compared to what humans would do.

## Links

- Past reports
- GR12: https://hackmd.io/HXQ3yWfBQN6QGQX4cmOAxQ
- GR11: https://hackmd.io/kOEbdDNeSdCkYXGY2wJExQ
- GR10: https://hackmd.io/XdCMH5DtRHyPNLCGDFZ-oQ
- GR09: https://hackmd.io/smLQ-QXdTse2dz1c6-Z85w

## Parameters

Algorithm Aggressiveness: 30%
Threshold for marking human evals as "Sybil or "Not Sybil": 90%

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Algorithm Aggressiveness tunes how much sensitive / specific the flagging model should be. When it is set to 50%, then it will be optimized for accuracy, while closer values to 0% means that it will be optimized for minimizing false detection rate
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## Statistics

### Sybil Incidence & Detection

#### Sybil Incidence

*Estimated Sybil Incidence per Evaluation Round*

- **Estimated Sybil Incidence: 14.1% +/- 1.3% (95% CI)**
- Estimated Sybil Users: 2453 (between 2227 and 2680, 95% CI)

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- **Flagged Users Fraction: 11.9%**
- Flagged Sybil Users: 2071

#### Sybil Flags

- ML generated flags: 53
- Heuristic generated flags: 1067
- Human provided flags: 951
- Total flags: 2071

#### Sybil Evaluations

*Relative distributions of scores per category*
`TODO`: upload histogram

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Total users evaluated by humans: 6405 (36.8% of total)
Users marked as true by humans: 951 (14.8%)
Users marked as false by humans: 5454 (85.2%)
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Total users evaluated by heuristics: 1180 (6.8% of total)
Users marked as true by heuristics: 1067 (90.4%)
Users marked as false by heuristics: 113 (9.6%)
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Total users evaluated by algorithms: 9818 (56.4% of total)
Users marked as true by algorithms: 53 (0.5%)
Users marked as false by algorithms: 9765 (99.5%)
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Total users evaluated: 17403 (100.0% of total)
Users marked as true: 2071 (11.9%)
Users marked as false: 15332 (88.1%)
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*Comparison between Evaluation / Prediction / Aggregate score*

### Summary Statistics on Scenarios

#### Contributors
Total Contributions (original): `TODO`
Total Contributions (modified): `TODO`
Total Contributions (removed): `TODO`
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Matched Contributions (original): `TODO`
Matched Contributions (modified): `TODO`
Matched Contributions (removed): `TODO`
Change: `TODO`
Total Contributors (original): `TODO`
Total Contributors (modified): `TODO`
Total Contributors (removed): `TODO`
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Sum of USDT Amount (original): `TODO`
Sum of USDT Amount (removed): `TODO`
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Median of Median USDT Amount per User (original): `TODO`
Median of Median USDT Amount per User (modified): `TODO`
Median of Median USDT Amount per User (removed): `TODO`
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Median Contribution Count per User (original): `TODO`
Median Contribution Count per User (modified): `TODO`
Median Contribution Count per User (removed): `TODO`
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Mean Count per User (original): `TODO`
Mean Count per User (modified): `TODO`
Mean Count per User (removed): `TODO`
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*Histogram of the Contribution per User aggregates*