papers/zoo-tokenomics/sections/03-airdrop-execution-mechanics.tex

\section{Airdrop Execution Mechanics}


\subsection{Pre-Airdrop Preparation}

Zoo Labs Foundation spent 18 months (March 2024 - September 2025) preparing for fair distribution:

\begin{enumerate}
\item \textbf{Contribution Tracking System}: Built transparent on-chain record of:
    \begin{itemize}
    \item GitHub commits (code contributions)
    \item Dataset uploads (data contributions)
    \item Feedback submissions (UX improvements)
    \item Community forum participation (engagement)
    \item Referrals (growth contributions)
    \end{itemize}

\item \textbf{Sybil Resistance Mechanisms}:
    \begin{itemize}
    \item Multi-factor identity verification (GitHub, Ethereum address, email, Discord)
    \item Minimum contribution thresholds (1 dataset upload OR 5 commits OR 50 forum posts)
    \item Anti-bot CAPTCHAs for critical actions
    \item Social graph analysis (isolated accounts flagged for manual review)
    \end{itemize}

\item \textbf{Community Education Campaign}:
    \begin{itemize}
    \item 20+ educational webinars on tokenomics
    \item Documentation in 15 languages
    \item "How to Contribute" guides and tutorials
    \item Transparent scoring dashboard (users saw allocation estimates pre-airdrop)
    \end{itemize}

\item \textbf{Legal Compliance}:
    \begin{itemize}
    \item 501(c)(3) legal review ensuring airdrop = educational/charitable distribution
    \item OFAC sanctions compliance (blocked regions excluded)
    \item Anti-money laundering checks
    \item No sale characterization (tokens distributed for free)
    \end{itemize}
\end{enumerate}

\subsection{Snapshot and Scoring Algorithm}

On August 15, 2025 (00:00 UTC), Zoo took a snapshot of all contribution data. The scoring algorithm proceeded in four stages:

\subsubsection{Stage 1: Raw Contribution Scores}

For each participant $i$:

\begin{align}
E_i^{\text{raw}} &= \log(1 + \text{days\_active}_i) \times \text{activity\_frequency}_i \\
D_i^{\text{raw}} &= \sum_{j} q_j \times v_j \quad \text{(dataset quality $q$ × volume $v$)} \\
C_i^{\text{raw}} &= \sum_{k} l_k \times r_k \quad \text{(lines of code $l$ × review score $r$)} \\
R_i^{\text{raw}} &= \sqrt{\text{valid\_referrals}_i} \quad \text{(sub-linear to discourage spam)}
\end{align}

\subsubsection{Stage 2: Normalization}

Normalize each category to [0, 1]:

\begin{equation}
E_i = \frac{E_i^{\text{raw}} - \min(E^{\text{raw}})}{\max(E^{\text{raw}}) - \min(E^{\text{raw}})}
\end{equation}

(Similarly for $D_i$, $C_i$, $R_i$)

\subsubsection{Stage 3: Weighted Composite Score}

\begin{equation}
S_i = 0.30 E_i + 0.40 D_i + 0.20 C_i + 0.10 R_i
\end{equation}

\subsubsection{Stage 4: Token Allocation}

Apply power-law curve to prevent extreme concentration:

\begin{equation}
A_i = \left( \frac{S_i}{\sum_{j} S_j} \right)^{0.7} \times 10^{12}
\end{equation}

The exponent 0.7 < 1 compresses the distribution, ensuring even low-scoring participants receive meaningful allocations.

\subsection{Airdrop Claim Process}

Zoo implemented a \textbf{pull-based claim system} rather than push distribution:

\begin{enumerate}
\item \textbf{Merkle Tree Publication} (Sept 1, 2025): Root hash published on-chain with IPFS backup
\item \textbf{Claim Portal Launch} (Sept 7, 2025): Web interface + CLI tool for claiming
\item \textbf{Claim Period}: 90 days (Sept 7 - Dec 5, 2025)
\item \textbf{Unclaimed Tokens}: Returned to DAO treasury after deadline
\end{enumerate}

\textbf{Rationale:} Pull-based claiming:
\begin{itemize}
\item Reduces gas costs (users pay own fees)
\item Proves ownership (claims require signature)
\item Filters disengaged recipients (unclaimed tokens recycled)
\item Enables gradual network load (avoids day-1 congestion)
\end{itemize}

As of October 28, 2025:
\begin{itemize}
\item 72\% of eligible tokens claimed (720B $\KEEPER$)
\item 87,341 total eligible → 62,885 claimed (72\% claim rate)
\item Remaining: 28 days until deadline
\item Expected final claim rate: 85-90\%
\end{itemize}

\subsection{Anti-Sybil Effectiveness}

Post-airdrop analysis revealed:
\begin{itemize}
\item 2,847 accounts flagged as potential Sybils (3.3\%)
\item Manual review: 412 confirmed Sybils (0.5\% of total)
\item Tokens reallocated: 4.2B $\KEEPER$ returned to treasury
\item False positive rate: 0.1\% (independent appeals board)
\end{itemize}

This demonstrates that multi-factor verification successfully prevented large-scale gaming while maintaining fairness.