MJos Predictive Model | First manifestation of the Bangel Language
New project sequence ยท proposed bindings
Current source package exposes arbitration, modes, noise filtering, loop protection, integrated pipeline and RecoveryStore. Pure Bangel decision source plus bounded Python host supervision. L1 observation, L2 association, L3 recall, L4 forecasting, L5 arbitration is explicitly a proposed logical organization.
MJ Decision Layer and recovery / Rainbow MJos Predictive Model | First manifestation of the Bangel Language MJ Physics Engineering | Owned and operated by Michael Bangel | Clearwater, Florida CROSS-SYSTEM PATH Human purpose -> MJ scope -> Bangel expression -> Elsa compilation -> JP admission -> Joanna execution evidence -> applicable independent review -> bounded result. PROJECT ADAPTER Current source package exposes arbitration, modes, noise filtering, loop protection, integrated pipeline and RecoveryStore. Pure Bangel decision source plus bounded Python host supervision. L1 observation, L2 association, L3 recall, L4 forecasting, L5 arbitration is explicitly a proposed logical organization. - Candidate: identity + score + source - Mode: separate from decision status - Health/Review/Minority/Downstream: independently bound - Recovery: operation ID + checkpoint + outcome certainty ADAPTER OBLIGATIONS Required reviews, liveness, forecast and downstream gates are present and current. Apply gate precedence, bounded rankings and progress-aware recovery with durable operation identities. Select an advisory or HOLD/DENY; ambiguous external completion remains held. RELATIONSHIP STATUS These links describe a proposed integration unless the cited source explicitly implements it. In particular, grouping BarTide or Moana under the MJos model does not prove an installed Bangel runtime in those products. Preserve type, clock, tenant and evidence provenance across every boundary. EVIDENCE LIMITS - SELECTED identifies an advisory candidate; no financial or physical permission. - Missing independent gates HOLD. - Recovery does not retry uncertain external effects and cannot convert predicted data into observed evidence.