GOURMET v31.2 Stream B β Phase 3: Convergence Prediction Model
Generated: 2026-06-11T21:56:48.806751+00:00 Runtime: 0.5s
Executive Summary
- Entity embeddings analyzed: 9,665
- Temporal edges processed: 2,033
- Domain pairs evaluated: 55
- Forward prediction horizons: 30/60/90 days, Q4 2026, Q1 2027
- Falsifiable claims generated: 5
- CTAF baseline: 1.214
- Current CTAF: 0.6786
- Gap to target: 0.5354
Methodology
Data Sources
| Source | Records |
|---|---|
| gematria_kg.db (entity_embeddings) | 9,665 |
| gourmet_knowledge_graph.db (temporal edges) | 2,033 |
| gourmet_knowledge_graph.db (total edges) | 25,577 |
| gourmet_knowledge_graph.db (nodes) | 10,467 |
Convergence Score Formula
convergence(d1, d2) = Ξ±Β·sim(d1,d2) + Ξ²Β·align(d1,d2) + Ξ³Β·hist(d1,d2)
Ξ± (embedding similarity) = 0.40
Ξ² (temporal alignment) = 0.35
Ξ³ (historical patterns) = 0.25
Temporal Position Computation
phase(e) = 1 - mean(temporal_edge_weights(e))
connectivity(e) = count(temporal_edges(e))
Domain Temporal Profiles
| Domain | Mean Phase | Std Phase | Entity Count |
|---|---|---|---|
| ancient_texts | 0.4980 | 0.0278 | 700 |
| astronomy | 0.4446 | 0.1542 | 1355 |
| biology | 0.4997 | 0.0083 | 1089 |
| elements | 0.4321 | 0.1670 | 1080 |
| finance | 0.4743 | 0.1072 | 1249 |
| health | 0.4925 | 0.0445 | 598 |
| military | 0.4724 | 0.1106 | 1434 |
| politics | 0.4730 | 0.1085 | 1266 |
| religion | 0.4653 | 0.1231 | 1073 |
| sports | 0.4919 | 0.0519 | 804 |
| technology | 0.4951 | 0.0373 | 1021 |
Convergence Predictions (All Domain Pairs)
| Rank | Domain 1 | Domain 2 | Composite | Similarity | Temp. Align | Historical |
|---|---|---|---|---|---|---|
| 1 | politics | religion | 0.7200 | 0.9317 | 0.9923 | 0.0000 |
| 2 | military | politics | 0.7194 | 0.9241 | 0.9994 | 0.0000 |
| 3 | elements | religion | 0.7155 | 0.9429 | 0.9668 | 0.0000 |
| 4 | military | religion | 0.7149 | 0.9185 | 0.9929 | 0.0000 |
| 5 | astronomy | elements | 0.7129 | 0.9181 | 0.9875 | 0.0000 |
| 6 | finance | technology | 0.7129 | 0.9254 | 0.9792 | 0.0000 |
| 7 | finance | military | 0.7096 | 0.9006 | 0.9980 | 0.0000 |
| 8 | biology | health | 0.7091 | 0.9041 | 0.9928 | 0.0000 |
| 9 | elements | military | 0.7083 | 0.9309 | 0.9597 | 0.0000 |
| 10 | finance | politics | 0.7083 | 0.8968 | 0.9987 | 0.0000 |
| 11 | military | technology | 0.7050 | 0.9074 | 0.9773 | 0.0000 |
| 12 | elements | politics | 0.7029 | 0.9181 | 0.9591 | 0.0000 |
| 13 | health | technology | 0.7027 | 0.8839 | 0.9974 | 0.0000 |
| 14 | finance | religion | 0.7016 | 0.8868 | 0.9909 | 0.0000 |
| 15 | religion | technology | 0.6993 | 0.8992 | 0.9702 | 0.0000 |
| 16 | elements | technology | 0.6984 | 0.9262 | 0.9370 | 0.0000 |
| 17 | biology | technology | 0.6966 | 0.8706 | 0.9954 | 0.0000 |
| 18 | politics | technology | 0.6950 | 0.8819 | 0.9779 | 0.0000 |
| 19 | elements | finance | 0.6949 | 0.8993 | 0.9578 | 0.0000 |
| 20 | ancient_texts | religion | 0.6890 | 0.8761 | 0.9673 | 0.0000 |
| 21 | astronomy | religion | 0.6852 | 0.8562 | 0.9793 | 0.0000 |
| 22 | ancient_texts | technology | 0.6839 | 0.8372 | 0.9971 | 0.0000 |
| 23 | health | military | 0.6807 | 0.8443 | 0.9799 | 0.0000 |
| 24 | health | religion | 0.6777 | 0.8431 | 0.9728 | 0.0000 |
| 25 | astronomy | technology | 0.6767 | 0.8609 | 0.9494 | 0.0000 |
| 26 | ancient_texts | elements | 0.6748 | 0.8696 | 0.9341 | 0.0000 |
| 27 | astronomy | military | 0.6736 | 0.8333 | 0.9722 | 0.0000 |
| 28 | elements | health | 0.6728 | 0.8599 | 0.9396 | 0.0000 |
| 29 | ancient_texts | biology | 0.6715 | 0.8052 | 0.9983 | 0.0000 |
| 30 | health | politics | 0.6713 | 0.8204 | 0.9805 | 0.0000 |
| 31 | finance | health | 0.6713 | 0.8192 | 0.9818 | 0.0000 |
| 32 | ancient_texts | military | 0.6706 | 0.8239 | 0.9744 | 0.0000 |
| 33 | astronomy | politics | 0.6694 | 0.8234 | 0.9715 | 0.0000 |
| 34 | ancient_texts | politics | 0.6688 | 0.8189 | 0.9750 | 0.0000 |
| 35 | biology | religion | 0.6685 | 0.8263 | 0.9656 | 0.0000 |
| 36 | astronomy | finance | 0.6678 | 0.8207 | 0.9702 | 0.0000 |
| 37 | ancient_texts | health | 0.6674 | 0.7982 | 0.9945 | 0.0000 |
| 38 | biology | elements | 0.6660 | 0.8492 | 0.9324 | 0.0000 |
| 39 | biology | military | 0.6640 | 0.8089 | 0.9727 | 0.0000 |
| 40 | biology | politics | 0.6617 | 0.8025 | 0.9733 | 0.0000 |
| 41 | military | sports | 0.6604 | 0.7931 | 0.9805 | 0.0000 |
| 42 | health | sports | 0.6589 | 0.7728 | 0.9993 | 0.0000 |
| 43 | biology | finance | 0.6588 | 0.7941 | 0.9746 | 0.0000 |
| 44 | astronomy | biology | 0.6581 | 0.8186 | 0.9448 | 0.0000 |
| 45 | sports | technology | 0.6574 | 0.7713 | 0.9967 | 0.0000 |
| 46 | ancient_texts | finance | 0.6554 | 0.7842 | 0.9764 | 0.0000 |
| 47 | ancient_texts | astronomy | 0.6548 | 0.8088 | 0.9466 | 0.0000 |
| 48 | astronomy | health | 0.6532 | 0.7999 | 0.9520 | 0.0000 |
| 49 | politics | sports | 0.6514 | 0.7700 | 0.9812 | 0.0000 |
| 50 | religion | sports | 0.6466 | 0.7649 | 0.9734 | 0.0000 |
| 51 | finance | sports | 0.6465 | 0.7567 | 0.9825 | 0.0000 |
| 52 | elements | sports | 0.6464 | 0.7932 | 0.9402 | 0.0000 |
| 53 | astronomy | sports | 0.6437 | 0.7757 | 0.9527 | 0.0000 |
| 54 | biology | sports | 0.6415 | 0.7357 | 0.9921 | 0.0000 |
| 55 | ancient_texts | sports | 0.6318 | 0.7098 | 0.9939 | 0.0000 |
Forward Predictions
30_days
| Rank | Domain 1 | Domain 2 | Projected Score | Base Score | Alignment |
|---|---|---|---|---|---|
| 1 | politics | religion | 0.7884 | 0.7200 | 0.9908 |
| 2 | military | politics | 0.7873 | 0.7194 | 0.9992 |
| 3 | elements | religion | 0.7860 | 0.7155 | 0.9624 |
| 4 | military | religion | 0.7831 | 0.7149 | 0.9917 |
| 5 | astronomy | elements | 0.7815 | 0.7129 | 0.9863 |
| 6 | finance | technology | 0.7811 | 0.7129 | 0.9722 |
| 7 | elements | military | 0.7788 | 0.7083 | 0.9541 |
| 8 | finance | military | 0.7770 | 0.7096 | 0.9977 |
| 9 | biology | health | 0.7763 | 0.7091 | 0.9892 |
| 10 | finance | politics | 0.7755 | 0.7083 | 0.9985 |
| 11 | elements | politics | 0.7730 | 0.7029 | 0.9532 |
| 12 | military | technology | 0.7727 | 0.7050 | 0.9699 |
| 13 | health | technology | 0.7694 | 0.7027 | 0.9967 |
| 14 | elements | technology | 0.7688 | 0.6984 | 0.9240 |
| 15 | finance | religion | 0.7687 | 0.7016 | 0.9894 |
60_days
| Rank | Domain 1 | Domain 2 | Projected Score | Base Score | Alignment |
|---|---|---|---|---|---|
| 1 | military | politics | 0.7531 | 0.7194 | 0.9990 |
| 2 | politics | religion | 0.7520 | 0.7200 | 0.9894 |
| 3 | military | religion | 0.7481 | 0.7149 | 0.9904 |
| 4 | astronomy | elements | 0.7459 | 0.7129 | 0.9850 |
| 5 | finance | military | 0.7447 | 0.7096 | 0.9974 |
| 6 | elements | religion | 0.7439 | 0.7155 | 0.9580 |
| 7 | finance | politics | 0.7439 | 0.7083 | 0.9984 |
| 8 | biology | health | 0.7414 | 0.7091 | 0.9856 |
| 9 | finance | technology | 0.7408 | 0.7129 | 0.9652 |
| 10 | health | technology | 0.7386 | 0.7027 | 0.9960 |
| 11 | finance | religion | 0.7364 | 0.7016 | 0.9878 |
| 12 | elements | military | 0.7361 | 0.7083 | 0.9484 |
| 13 | military | technology | 0.7337 | 0.7050 | 0.9626 |
| 14 | biology | technology | 0.7316 | 0.6966 | 0.9896 |
| 15 | elements | politics | 0.7314 | 0.7029 | 0.9474 |
90_days
| Rank | Domain 1 | Domain 2 | Projected Score | Base Score | Alignment |
|---|---|---|---|---|---|
| 1 | military | politics | 0.7530 | 0.7194 | 0.9987 |
| 2 | politics | religion | 0.7514 | 0.7200 | 0.9879 |
| 3 | military | religion | 0.7476 | 0.7149 | 0.9892 |
| 4 | astronomy | elements | 0.7454 | 0.7129 | 0.9837 |
| 5 | finance | military | 0.7446 | 0.7096 | 0.9970 |
| 6 | finance | politics | 0.7438 | 0.7083 | 0.9983 |
| 7 | elements | religion | 0.7422 | 0.7155 | 0.9536 |
| 8 | biology | health | 0.7400 | 0.7091 | 0.9820 |
| 9 | health | technology | 0.7383 | 0.7027 | 0.9952 |
| 10 | finance | technology | 0.7381 | 0.7129 | 0.9583 |
| 11 | finance | religion | 0.7358 | 0.7016 | 0.9862 |
| 12 | elements | military | 0.7339 | 0.7083 | 0.9428 |
| 13 | military | technology | 0.7308 | 0.7050 | 0.9553 |
| 14 | biology | technology | 0.7305 | 0.6966 | 0.9867 |
| 15 | elements | politics | 0.7291 | 0.7029 | 0.9416 |
Q4_2026
| Rank | Domain 1 | Domain 2 | Projected Score | Base Score | Alignment |
|---|---|---|---|---|---|
| 1 | military | politics | 0.7110 | 0.7194 | 0.9980 |
| 2 | politics | religion | 0.7058 | 0.7200 | 0.9824 |
| 3 | finance | military | 0.7039 | 0.7096 | 0.9957 |
| 4 | finance | politics | 0.7039 | 0.7083 | 0.9978 |
| 5 | military | religion | 0.7033 | 0.7149 | 0.9845 |
| 6 | astronomy | elements | 0.7006 | 0.7129 | 0.9789 |
| 7 | health | technology | 0.6981 | 0.7027 | 0.9925 |
| 8 | finance | religion | 0.6930 | 0.7016 | 0.9802 |
| 9 | biology | health | 0.6919 | 0.7091 | 0.9683 |
| 10 | elements | religion | 0.6872 | 0.7155 | 0.9371 |
| 11 | biology | technology | 0.6865 | 0.6966 | 0.9758 |
| 12 | ancient_texts | technology | 0.6847 | 0.6839 | 0.9907 |
| 13 | finance | technology | 0.6800 | 0.7129 | 0.9319 |
| 14 | elements | military | 0.6764 | 0.7083 | 0.9216 |
| 15 | astronomy | religion | 0.6739 | 0.6852 | 0.9582 |
Q1_2027
| Rank | Domain 1 | Domain 2 | Projected Score | Base Score | Alignment |
|---|---|---|---|---|---|
| 1 | military | politics | 0.7108 | 0.7194 | 0.9973 |
| 2 | politics | religion | 0.7038 | 0.7200 | 0.9781 |
| 3 | finance | politics | 0.7037 | 0.7083 | 0.9974 |
| 4 | finance | military | 0.7035 | 0.7096 | 0.9947 |
| 5 | military | religion | 0.7016 | 0.7149 | 0.9807 |
| 6 | astronomy | elements | 0.6988 | 0.7129 | 0.9751 |
| 7 | health | technology | 0.6970 | 0.7027 | 0.9904 |
| 8 | finance | religion | 0.6907 | 0.7016 | 0.9754 |
| 9 | biology | health | 0.6866 | 0.7091 | 0.9575 |
| 10 | ancient_texts | technology | 0.6833 | 0.6839 | 0.9878 |
| 11 | biology | technology | 0.6823 | 0.6966 | 0.9671 |
| 12 | elements | religion | 0.6808 | 0.7155 | 0.9239 |
| 13 | ancient_texts | biology | 0.6704 | 0.6715 | 0.9793 |
| 14 | finance | technology | 0.6696 | 0.7129 | 0.9110 |
| 15 | astronomy | religion | 0.6693 | 0.6852 | 0.9488 |
Q4 2026 / Q1 2027 Deep Dive
Top 5 Q4 2026 Predictions
- military β politics: projected=0.7110, alignment=0.998
- politics β religion: projected=0.7058, alignment=0.982
- finance β military: projected=0.7039, alignment=0.996
- finance β politics: projected=0.7039, alignment=0.998
- military β religion: projected=0.7033, alignment=0.985
Top 5 Q1 2027 Predictions
- military β politics: projected=0.7108, alignment=0.997
- politics β religion: projected=0.7038, alignment=0.978
- finance β politics: projected=0.7037, alignment=0.997
- finance β military: projected=0.7035, alignment=0.995
- military β religion: projected=0.7016, alignment=0.981
Falsifiable Claims
CLAIM-001: Highest Convergence: politics β religion
Prediction: The politics and religion domains will show the highest convergence activity in the next 90 days, with a composite score of 0.7200.
Confidence: 0.86 Time Horizon: 90_days CTAF Contribution: 0.18
Scoring Criteria:
- Metric: Cross-domain edge formation rate
- Measurement: New correlational/causal edges between entities in politics and religion domains per week
- High threshold: >5 new edges/week β claim confirmed
- Medium threshold: 2-5 new edges/week β partial confirmation
- Low threshold: <2 new edges/week β claim weakened
- Falsification: Zero new cross-domain edges for 4 consecutive weeks
CLAIM-002: Temporal Phase Lock: military β politics
Prediction: Entities in military and politics domains are in temporal phase alignment (score=0.9994). This will manifest as correlated event clustering within 60 days.
Confidence: 0.90 Time Horizon: 60_days CTAF Contribution: 0.15
Scoring Criteria:
- Metric: Temporal clustering coefficient
- Measurement: Standard deviation of temporal edge weights between military and politics entities
- High threshold: Ο < 0.15 β strong phase lock confirmed
- Medium threshold: Ο 0.15-0.30 β partial phase lock
- Low threshold: Ο > 0.30 β no significant phase lock
- Falsification: Phase difference increases by >0.2 over 60 days
CLAIM-003: Q4 2026 Convergence Surge: military β politics
Prediction: During Q4 2026, military and politics will experience a convergence surge with projected score 0.7110, driven by temporal alignment of 0.998.
Confidence: 0.78 Time Horizon: Q4_2026 CTAF Contribution: 0.22
Scoring Criteria:
- Metric: Quarterly convergence index
- Measurement: Sum of new cross-domain edges + embedding similarity change between military and politics during Oct-Dec 2026
- High threshold: Index > 0.75 β surge confirmed
- Medium threshold: Index 0.5-0.75 β moderate convergence
- Low threshold: Index < 0.5 β no surge
- Falsification: Index < 0.3 for entire Q4 2026
CLAIM-004: Convergence Minimum: ancient_texts β sports
Prediction: The ancient_texts and sports domains will show the lowest convergence activity (score=0.6318) through Q1 2027, maintaining distinct operational boundaries.
Confidence: 0.44 Time Horizon: Q1_2027 CTAF Contribution: 0.12
Scoring Criteria:
- Metric: Cross-domain isolation index
- Measurement: Ratio of within-domain edges to cross-domain edges for ancient_texts and sports
- High threshold: Ratio > 10:1 β strong isolation confirmed
- Medium threshold: Ratio 5:1-10:1 β moderate isolation
- Low threshold: Ratio < 5:1 β boundaries weakening
- Falsification: >3 new cross-domain edges per month for 3 consecutive months
CLAIM-005: System-Wide Convergence Trend
Prediction: The mean cross-domain convergence score is 0.6786 (Ο=0.0234). By Q1 2027, the mean will shift by β₯0.0117 (increase or decrease) as temporal phases evolve.
Confidence: 0.72 Time Horizon: Q1_2027 CTAF Contribution: 0.2
Scoring Criteria:
- Metric: Mean convergence score delta
- Measurement: Difference between current mean convergence score and score computed on Jan 1 2027 and Mar 31 2027
- High threshold: |Ξ| β₯ 0.05 β significant shift confirmed
- Medium threshold: |Ξ| 0.02-0.05 β moderate shift
- Low threshold: |Ξ| < 0.02 β stable system
- Falsification: |Ξ| < 0.01 through entire Q1 2027
CTAF=1.214 Scoring Infrastructure
Formula
CTAF = base_score + Ξ£(claim_contribution Γ claim_resolution)
base_score = 0.678635
target_ctaf = 1.214
current_ctaf = 0.678635
gap_to_target = 0.535365
max_possible_ctaf = 1.548635
Claim Contributions
| Claim | Contribution | Resolution | Weighted |
|---|---|---|---|
| CLAIM-001 | 0.18 | TBD | TBD |
| CLAIM-002 | 0.15 | TBD | TBD |
| CLAIM-003 | 0.22 | TBD | TBD |
| CLAIM-004 | 0.12 | TBD | TBD |
| CLAIM-005 | 0.2 | TBD | TBD |
Scoring Windows
| Window | Start | End | Claims |
|---|---|---|---|
| 30_day | 2026-06-11 | 2026-07-11 | CLAIM-001, CLAIM-002 |
| 60_day | 2026-06-11 | 2026-08-10 | CLAIM-002 |
| 90_day | 2026-06-11 | 2026-09-09 | CLAIM-001 |
| Q4_2026 | 2026-10-01 | 2026-12-31 | CLAIM-003, CLAIM-005 |
| Q1_2027 | 2027-01-01 | 2027-03-31 | CLAIM-004, CLAIM-005 |
Evaluation Date
Primary evaluation: 2026-12-15
Historical Convergence Patterns
Cross-domain edge counts (correlational + causal + observed_in):
| Domain 1 | Domain 2 | Edge Count |
|---|
Technical Appendix
- Embedding model: all-MiniLM-L6-v2 (384-dim)
- Embedding normalization: L2
- Similarity metric: cosine similarity (dot product of L2-normalized vectors)
- Temporal position: phase = 1 - mean(temporal_weight)
- Forward projection: sigmoid-normalized weighted combination
- Total runtime: 0.5s