Glossary

This score measures how clearly the current scorecard and driver evidence support the label that was chosen. It is the signal-clarity side of confidence.

For non-STABLE labels, label confidence mainly depends on scorecard margin and driver support. For STABLE, the model also rewards neutrality, because a stable label should look genuinely close to the chain's own middle ground rather than merely lacking extreme readings. UNKNOWN/DEGRADED maps to zero label confidence.

This is a derived signal showing how unusually unstable block timing has recently been. It focuses on irregularity rather than raw speed.

The backend uses a special instability scoring routine for avg_block_time_sec rather than treating the raw value as a standard monotonic signal. That makes this component a better fit for Capacity pressure, because erratic block production can indicate strain even when average block time alone is not obviously extreme.

The number of transactions observed on the chain for that day. It is one of the clearest direct activity measures: more transactions usually means more throughput demand, though not all transactions are economically equal.

tx_count_daily feeds the Demand axis. In the scorecard it is log-transformed before standardized scoring so very large absolute chains do not dominate purely because of scale. It should be interpreted alongside active addresses and tx_per_user rather than as a standalone demand truth.

Which high-level dimension the driver belongs to: demand, friction, or capacity. This tells you what kind of pressure the metric is describing.

The axis field is part of the published explanation payload and should be interpreted as the metric's role inside the scorecard/regime model, not just a cosmetic tag. It lets advanced users trace local signals back to the dimension-level classification logic.

The raw current value of the driver metric. This is the actual metric reading before it is translated into z-scores, percentiles, or scorecard scores.

Raw current values are crucial for traceability because they let advanced users move from explanation back to the underlying observed number. Correct interpretation depends on the metric's own unit definition, not on the driver wrapper itself.

Fee burden proxy is a normalized fee measure: roughly speaking, how large the typical fee is relative to the typical transaction value. It is meant to reflect how 'heavy' fees feel, not just how high they are in absolute token terms.

The backend computes fee_burden_proxy as median_tx_fee_native divided by median_tx_value_native when both are available. It is then log-transformed and standardized into the Friction dimension. This is a better user-cost proxy than raw fee alone because it makes cost interpretable relative to typical economic activity size.

Transactions per active address is a simple intensity measure: how much activity is occurring per active participant. It helps distinguish broad light usage from concentrated heavy usage.

The backend derives tx_per_user as tx_count_daily divided by unique_active_addresses, with zero denominators treated as missing. It is part of the Demand score because a chain where each active address is doing much more than usual can signal demand pressure even if absolute address count alone does not look extreme.

STABLE means the chain does not currently show a strong enough combination of demand pressure, friction pressure, or cheap-capacity conditions to justify a more extreme label. It does not mean 'nothing is happening'. It means the chain still looks broadly within its normal historical operating range.

In the ruleset, STABLE is the default label when the evidence does not meet CONGESTED, CHEAP, or HEATING conditions and the confidence gate does not force UNKNOWN/DEGRADED. In practice this usually means scorecard dimensions are not far enough from neutral, or the directional evidence is not persistent enough, to support a stronger regime label.

A 0-100 score describing how tight the chain's capacity conditions look. Higher means the chain appears closer to practical throughput pressure. Lower means more room to spare.

Capacity is built from gas_utilization_pct and blocktime_instability. The product uses 'capacity' to mean pressure on usable execution capacity, not installed theoretical capacity. Like the other dimensions, the final score is pulled toward 50 when effective confidence is low.

Coverage factor tells you how many of an axis's expected components were actually available. A lower value means that axis had to be judged with fewer than the ideal supporting inputs.

Each scorecard dimension has an expected component count: Demand expects 3, Friction expects 2, Capacity expects 2. coverage_factor is used together with overall confidence to form effective_confidence for that axis. This is why a dimension can stay visible but become visibly less assertive when component coverage is incomplete.

A 0-100 score describing how hot the chain's demand side looks relative to its own history. Around 50 is neutral. Higher means more demand pressure. Lower means quieter conditions.

Demand is built from tx_count_daily, unique_active_addresses, and tx_per_user. The raw component scores are combined and then shrunk back toward 50 according to effective confidence. This means high demand scores require both strong signals and enough confidence to trust them.

Effective confidence is the amount of confidence that actually reaches a single scorecard axis after taking that axis's coverage into account.

The backend computes effective_confidence as base_confidence × coverage_factor for each dimension. The final displayed score is then moved back toward 50 using this value. That is why low effective confidence does not necessarily delete a score; instead it makes the score less extreme and therefore more conservative.

A 0-100 score describing how difficult or expensive the chain currently looks to use relative to its own history. Higher means more cost or execution friction. Lower means the chain looks easier to use.

Friction is built from fee_burden_proxy and failed_tx_rate. The important subtlety is that this is not just a fee level. It is a composite pressure view of cost and failure-like strain, expressed relative to the chain's own normal behavior and shrunk toward 50 when confidence is weak.

A built-in note explaining how to read the scorecard. The core idea is simple: scores are 0-100, 50 is neutral versus the chain's own history, and low confidence pulls scores back toward 50.

This note is important because it encodes the product's central score semantics: chain-relative normalization, 50 as neutral midpoint, and confidence-aware shrinkage. Those three ideas are what stop the scorecard from being mistaken for an absolute cross-chain ranking.

The qualitative band attached to a score, such as low, normal, or high. It makes the numeric score easier to read quickly.

Levels are not separate data; they are categorical interpretations of the underlying 0-100 score. The confidence logic also uses score-versus-level margin, because a label should be more trustworthy when the score sits well inside its assigned band rather than barely touching it.