BuckVisionAI BUCKVISIONAIHUNT EXECUTION + OUTCOME VALIDATION
BUCKVISIONAI 70.5 · HUNT EXECUTION + OUTCOME VALIDATION ENGINE V1

THE DECISION DOES NOT END WHEN YOU CLIMB INTO THE STAND

Close the Field Intelligence Loop.

Turn an exact saved HuntScore into a traceable field plan, record what actually happened, and compare the recorded outcome against the saved decision context. One hunt becomes evidence. It never becomes automatic proof that the model is right or wrong.

70.5 VALIDATION ENGINE READYLoading saved HuntScore and field history…
SAVED HUNTSCOREExact decision snapshot
FIELD EXECUTIONWhat actually happened
70.5 VALIDATIONAlignment, limits, compromises
CONTROLLED LEARNINGNo automatic weight changes
Planned HuntScore--
Plan Status--
Decision Alignment--
Evidence Strength--
70.5 FIELD PLAN

Select a saved HuntScore decision

The exact saved decision context will appear here before the field outcome is recorded.

PROPERTY--
STAND--
PLANNED DATE--
PLANNED WINDOW--
WEATHER PROVENANCE--
STRATEGY CONTEXT--
SAVED RECOMMENDATION --
SAVED RISK

--

01

Record The Actual Hunt

Hunter-recorded field evidence
70.5 DECISION VALIDATION
VALIDATION STATE

WAITING FOR OUTCOME

--ALIGNMENT

Record the actual hunt to compare the field outcome against the exact saved HuntScore decision.

EVIDENCE STRENGTH--
CONTROLLED LEARNING SIGNAL--
SUPPORTING EVIDENCE

No validation evidence yet.

LIMITS / CONFLICTS

No validation limits yet.

70.5 Learning Integrity Rule
A hunt outcome is evidence, not proof. A no-deer sit does not automatically disprove a high HuntScore. A harvest does not automatically prove every part of the model was correct. Wind shifts, contaminated access, major weather changes, or ignoring the saved recommendation are recorded as execution compromises. 70.5 never changes HuntScore weights or strategy logic automatically.
NEXT

70.6 Controlled Outcome Learning

Repeated outcomes, not one-hunt conclusions

After multiple 70.5 field outcomes are validated, 70.6 groups the repeated evidence by HuntScore band, stand, hunt window, season, provider provenance, and decision-factor contribution. Learning signals never change the active HuntScore model automatically.

Analyze Repeated Outcomes 70.6