Read the equation
Computes the implementation’s product of activation, learning rate and item confidence, matching the proposed Γ factor.
Terms & definitions
The recovered passage does not supply a separate symbol table. Read the source context for the notation used in this version; familiar letters may carry different meanings in other builds.
How it works
Read Gamma as the quantity being defined or updated by this relation.
Substitute the source-defined variables or recorded values and follow the displayed arithmetic and indexing exactly.
Use this version’s variable definitions and update order. The same symbol may be assigned a different meaning in another build.
Assumptions & limits
Source recovery establishes that the expression appears in this record. Its mathematical validity, implementation behavior, and interpretation must be assessed separately. See the linked source and research notes for the checks available for this version.
Original context
Read the surrounding source passage
// 3. Compute bounded plasticity
float eta = clamp(ETA_BASE + ETA_GAIN * item.kappa, ETA_MIN, ETA_MAX);
// 4. Learning drive
float Gamma = chi * eta * item.kappa;
// 5. Allocator computes π^eff across substrates
auto pi_eff = allocator.compute(item, state.families[f]);
// 6. Master update to each substrate
for (auto& [s, weight] : pi_eff) {
if (weight > 1e-6f) {Exact original notation
float Gamma = chi * eta * item.kappa;
Related builds
Sources & provenance
float Gamma = chi * eta * item.kappa;