How Contributions Are Recorded¶
Contributions in Moosh are recorded as signals that help the protocol understand real usage, network dynamics, and system behavior during the testnet phase.
Recording a contribution does not mean assigning points, rewards, or guarantees. It is a process of observing, classifying, and interpreting participant actions to inform system design and iteration.
System-Level Observation¶
Contribution recording is primarily driven by system-level observation.
On-chain actions, interaction patterns, and related activity are analyzed to understand how participants engage with the protocol. The system focuses on behavior over time rather than isolated events, and on context rather than raw counts.
Not all recorded actions are treated equally. Some interactions provide stronger or more reliable signals than others.
Automated Signals and Contextual Evaluation¶
Most contribution signals are captured automatically based on observable behavior. These signals may include, but are not limited to: - On-chain interactions with protocol contracts - Relationship patterns between participants - Usage sequences and timing - Consistency and continuity of activity
In certain cases, contribution recognition may involve contextual or qualitative evaluation. This is especially relevant for contributions related to system support, feedback, or edge-case discovery, where intent and usefulness cannot be inferred from on-chain data alone.
Quality Over Volume¶
Moosh emphasizes contribution quality over volume.
High-frequency actions, repetitive patterns, or behavior designed solely to inflate activity metrics may be deprioritized or excluded. The system is designed to recognize actions that provide meaningful insight into protocol behavior, risk boundaries, or user experience.
Non-Real-Time and Adjustable Logic¶
Contribution recording is not guaranteed to be real-time.
Signals may be processed with delay, aggregated over time, or reinterpreted as system understanding improves. Recording logic, thresholds, and classification rules may change during the testnet phase to better reflect testing goals and observed behavior.
Historical contribution records may be adjusted or recalculated when necessary.
No Guaranteed Outcomes¶
Recorded contributions do not imply rewards, ownership, status, or future entitlements.
Contribution tracking exists to support system learning and improvement. It should not be interpreted as a promise of incentives, rankings, or recognition beyond its role in the testnet process.
Summary¶
Contribution recording in Moosh is a flexible, evolving process designed to capture meaningful participation signals rather than enforce rigid metrics. Its purpose is to support responsible system iteration, not to create gamified behavior or speculative expectations.