{
 "page": "P117",
 "title": "Semantic deviation and Framework 15: pre-registered tests of model convergence",
 "description": "These claims pre-register tests of whether machine output converges and RLHF compresses deviation. The Semantic Deviation Research Program and Measuring Semantic Deviation predict negative signed deviation for AI text, a base-versus-instruct differential, stability across scale and agreement across judges. Framework 15 and its papers from Lagrange Observatory! freeze the 90-day retrieval-basin winding-number protocol and the deviation-optimized DPO experiment, with results deposits owed.",
 "line": "meaning-measurement",
 "url": "https://www.operativesemiotics.org/predictions/semantic-deviation-framework-15-model-convergence",
 "claims": [
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   "id": "PRED-686.B2",
   "tier": "forecast",
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   "registered": "2026-04-24",
   "registered_in": 686,
   "registered_title": "Mycelial Cardboard Boxes: Market Analysis and Production Strategy",
   "record": "https://www.alexanarch.org/s/records/686/",
   "page": "https://www.operativesemiotics.org/predictions/semantic-deviation-framework-15-model-convergence#PRED-686.B2",
   "statement": "The critical experiment is whether colonized cardboard survives folding — test this immediately with a small batch. If the mycelium heals across fold lines (likely, if folded while still actively growing), the product ships flat, stores efficiently, and the customer unfolds their own living box. That's a better experience than receiving a pre-formed box, and it's a better story.",
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     "record": "https://www.alexanarch.org/s/records/686/",
     "quote": "The critical experiment is whether colonized cardboard survives folding — test this immediately with a small batch. If the mycelium heals across fold lines (likely, if folded while still actively growing), the product ships flat, stores efficiently, and the customer unfolds their own living box. That's a better experience than receiving a pre-formed box, and it's a better story.",
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   "id": "PRED-107.F3",
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   "registered": "2026-05-17",
   "registered_in": 107,
   "registered_title": "Audited Claims for the Semantic Deviation Research Program — The Glas Function: An External-Format Restatement",
   "record": "https://www.alexanarch.org/s/records/107/",
   "page": "https://www.operativesemiotics.org/predictions/semantic-deviation-framework-15-model-convergence#PRED-107.F3",
   "statement": "The right experimental design is component-decomposed. Conventions:\n\n- **Model-Base** is the pre-fine-tune checkpoint of the chosen open-weight model (e.g., the base Llama-3.1-8B prior to any post-training).",
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   "statements": [
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     "find": "F107.F3",
     "deposit": 107,
     "date": "2026-05-17",
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     "shape": "F",
     "record": "https://www.alexanarch.org/s/records/107/",
     "quote": "The right experimental design is component-decomposed. Conventions:\n\n- **Model-Base** is the pre-fine-tune checkpoint of the chosen open-weight model (e.g., the base Llama-3.1-8B prior to any post-training).",
     "falsifier": null
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     "find": "F107.F8",
     "deposit": 107,
     "date": "2026-05-17",
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     "record": "https://www.alexanarch.org/s/records/107/",
     "quote": "| Model-Full (= Model-Sem) | on | on | on | on | on |\n\n\nThe single-component conditions (Model-π, Model-Dev, Model-Coh) isolate the uplift attributable to each signal. The full condition (Model-Full) replicates the design of the corpus's existing DPO protocol. The differences between conditions are the scientifically interesting quantities, not the absolute performance of the full condition.",
     "falsifier": null
    }
   ],
   "states": [],
   "evidence": [],
   "series": [
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  {
   "id": "PRED-107.F4",
   "tier": "unbuilt test",
   "shapes": [
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   "registered": "2026-05-17",
   "registered_in": 107,
   "registered_title": "Audited Claims for the Semantic Deviation Research Program — The Glas Function: An External-Format Restatement",
   "record": "https://www.alexanarch.org/s/records/107/",
   "page": "https://www.operativesemiotics.org/predictions/semantic-deviation-framework-15-model-convergence#PRED-107.F4",
   "statement": "**Black-box judge replacement test.** A scientifically essential robustness check, directly addressing the reward-model overoptimization concerns characterized in Gao et al. 2023: train Model-Sem against Judge-A; evaluate against Judge-B (a different frozen model with different weights). If Model-Sem's uplift persists against Judge-B, the result generalizes beyond the specific judge. If it disappears, the result is judge-specific reward hacking. This test is cheap (one additional evaluation pass) and load-bearing.",
   "falsifier": null,
   "window": null,
   "statements": [
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     "find": "F107.F4",
     "deposit": 107,
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     "shape": "F",
     "record": "https://www.alexanarch.org/s/records/107/",
     "quote": "**Black-box judge replacement test.** A scientifically essential robustness check, directly addressing the reward-model overoptimization concerns characterized in Gao et al. 2023: train Model-Sem against Judge-A; evaluate against Judge-B (a different frozen model with different weights). If Model-Sem's uplift persists against Judge-B, the result generalizes beyond the specific judge. If it disappears, the result is judge-specific reward hacking. This test is cheap (one additional evaluation pass) and load-bearing.",
     "falsifier": null
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   "evidence": [],
   "series": [
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  {
   "id": "PRED-107.G1",
   "tier": "forecast",
   "shapes": [
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    "G"
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   "registered": "2026-05-17",
   "registered_in": 107,
   "registered_title": "Audited Claims for the Semantic Deviation Research Program — The Glas Function: An External-Format Restatement",
   "record": "https://www.alexanarch.org/s/records/107/",
   "page": "https://www.operativesemiotics.org/predictions/semantic-deviation-framework-15-model-convergence#PRED-107.G1",
   "statement": "**P2 — Pre-RLHF vs. post-RLHF deviation differential.** Base models (Llama-3.1-8B base versus Llama-3.1-8B-Instruct) should exhibit different mean $\\bar{\\delta}$ on matched prompts. The framework predicts the chat-tuned model exhibits lower mean signed deviation — that RLHF produces convergence pressure measurable in the signed-deviation statistic. Same two-sided Mann-Whitney U at $\\alpha = 0.05$.",
   "falsifier": null,
   "window": null,
   "statements": [
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     "find": "F107.G1",
     "deposit": 107,
     "date": "2026-05-17",
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     "shape": "G",
     "record": "https://www.alexanarch.org/s/records/107/",
     "quote": "**P2 — Pre-RLHF vs. post-RLHF deviation differential.** Base models (Llama-3.1-8B base versus Llama-3.1-8B-Instruct) should exhibit different mean $\\bar{\\delta}$ on matched prompts. The framework predicts the chat-tuned model exhibits lower mean signed deviation — that RLHF produces convergence pressure measurable in the signed-deviation statistic. Same two-sided Mann-Whitney U at $\\alpha = 0.05$.",
     "falsifier": null
    },
    {
     "find": "F110.A2",
     "deposit": 110,
     "date": "2026-05-18",
     "lines": [
      356,
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     "shape": "A",
     "record": "https://www.alexanarch.org/s/records/110/",
     "quote": "**P2 (RLHF flattening).** Post-RLHF chat-tuned models exhibit lower $\\bar{\\delta}$ than their pre-RLHF base counterparts on matched prompts — consistent with Ouyang et al.'s (2022) observation that RLHF produces more uniform outputs. Tested on meta-llama/Llama-3.1-8B (base) vs. meta-llama/Llama-3.1-8B-Instruct (chat-tuned), evaluated on 100 prompts from the OpenAssistant dataset matched by length bin. Limited to open-weight models where base weights are available.",
     "falsifier": null
    }
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   "states": [],
   "evidence": [],
   "series": [
    "Semantic Deviation Research Program audited claims"
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   "register_aliases": [
    "I:063",
    "I:065"
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   "flags": [],
   "merged": []
  },
  {
   "id": "PRED-107.G2",
   "tier": "standing claim",
   "shapes": [
    "G"
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   "registered": "2026-05-17",
   "registered_in": 107,
   "registered_title": "Audited Claims for the Semantic Deviation Research Program — The Glas Function: An External-Format Restatement",
   "record": "https://www.alexanarch.org/s/records/107/",
   "page": "https://www.operativesemiotics.org/predictions/semantic-deviation-framework-15-model-convergence#PRED-107.G2",
   "statement": "A reasonable advance prediction, based on the structure of the framework and the existing alignment literature: **the provenance component will carry more uplift independently than the deviation component**.",
   "falsifier": "This is a falsifiable prediction. If Model-Dev outperforms Model-π in human preference ratings on a matched evaluation set, the deviation hypothesis is vindicated independently. If Model-π outperforms Model-Dev, the framework retains value but its center of gravity shifts: provenance-aware preference optimization becomes the durable contribution, while deviation becomes a secondary stabilizer.",
   "window": null,
   "statements": [
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     "find": "F107.G2",
     "deposit": 107,
     "date": "2026-05-17",
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     "shape": "G",
     "record": "https://www.alexanarch.org/s/records/107/",
     "quote": "A reasonable advance prediction, based on the structure of the framework and the existing alignment literature: **the provenance component will carry more uplift independently than the deviation component**.",
     "falsifier": "This is a falsifiable prediction. If Model-Dev outperforms Model-π in human preference ratings on a matched evaluation set, the deviation hypothesis is vindicated independently. If Model-π outperforms Model-Dev, the framework retains value but its center of gravity shifts: provenance-aware preference optimization becomes the durable contribution, while deviation becomes a secondary stabilizer."
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  {
   "id": "PRED-107.G4",
   "tier": "standing claim",
   "shapes": [
    "G"
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   "registered": "2026-05-17",
   "registered_in": 107,
   "registered_title": "Audited Claims for the Semantic Deviation Research Program — The Glas Function: An External-Format Restatement",
   "record": "https://www.alexanarch.org/s/records/107/",
   "page": "https://www.operativesemiotics.org/predictions/semantic-deviation-framework-15-model-convergence#PRED-107.G4",
   "statement": "**P3 — Effect size scaling.** The Slop vs. Human deviation differential should be stable or grow with model scale (computed across multiple model sizes within a family — e.g., Llama-3.1-8B-Instruct, 70B-Instruct, 405B-Instruct). If the differential disappears at scale, the framework's predictions are small-model artifacts.",
   "falsifier": null,
   "window": null,
   "statements": [
    {
     "find": "F107.G4",
     "deposit": 107,
     "date": "2026-05-17",
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      274,
      274
     ],
     "shape": "G",
     "record": "https://www.alexanarch.org/s/records/107/",
     "quote": "**P3 — Effect size scaling.** The Slop vs. Human deviation differential should be stable or grow with model scale (computed across multiple model sizes within a family — e.g., Llama-3.1-8B-Instruct, 70B-Instruct, 405B-Instruct). If the differential disappears at scale, the framework's predictions are small-model artifacts.",
     "falsifier": null
    }
   ],
   "states": [],
   "evidence": [],
   "series": [
    "Semantic Deviation Research Program audited claims"
   ],
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  },
  {
   "id": "PRED-107.G5",
   "tier": "forecast",
   "shapes": [
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    "G"
   ],
   "registered": "2026-05-17",
   "registered_in": 107,
   "registered_title": "Audited Claims for the Semantic Deviation Research Program — The Glas Function: An External-Format Restatement",
   "record": "https://www.alexanarch.org/s/records/107/",
   "page": "https://www.operativesemiotics.org/predictions/semantic-deviation-framework-15-model-convergence#PRED-107.G5",
   "statement": "**P4 — Cross-judge consistency.** The differential should replicate when computed against a different reference model. Test the same Slop/Human corpus against Mistral-7B-Instruct's logits; the Spearman rank correlation between per-output $\\bar{\\delta}$ rankings under Llama and Mistral should exceed 0.7. If it does not, the deviation statistic is judge-specific, and a much more careful argument is required to claim it measures anything intrinsic to the texts.",
   "falsifier": null,
   "window": null,
   "statements": [
    {
     "find": "F107.G5",
     "deposit": 107,
     "date": "2026-05-17",
     "lines": [
      277,
      277
     ],
     "shape": "G",
     "record": "https://www.alexanarch.org/s/records/107/",
     "quote": "**P4 — Cross-judge consistency.** The differential should replicate when computed against a different reference model. Test the same Slop/Human corpus against Mistral-7B-Instruct's logits; the Spearman rank correlation between per-output $\\bar{\\delta}$ rankings under Llama and Mistral should exceed 0.7. If it does not, the deviation statistic is judge-specific, and a much more careful argument is required to claim it measures anything intrinsic to the texts.",
     "falsifier": null
    },
    {
     "find": "F110.A3",
     "deposit": 110,
     "date": "2026-05-18",
     "lines": [
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     ],
     "shape": "A",
     "record": "https://www.alexanarch.org/s/records/110/",
     "quote": "**P3 (Cross-judge consistency).** The differential replicates under mistralai/Mistral-7B-Instruct-v0.3. Spearman rank correlation between per-output $\\bar{\\delta}$ rankings under Llama and Mistral exceeds 0.7. Failure indicates judge-specificity, not an intrinsic text property.",
     "falsifier": null
    }
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   "id": "PRED-108.F1",
   "tier": "unbuilt test",
   "shapes": [
    "F"
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   "registered": "2026-05-17",
   "registered_in": 108,
   "registered_title": "Framework 15 — Measurement of Meaning: Operations from Lagrange Observatory! within the Semantic Physics Matrix (EA-SEI-FW15-MANIFESTO v1.0)",
   "record": "https://www.alexanarch.org/s/records/108/",
   "page": "https://www.operativesemiotics.org/predictions/semantic-deviation-framework-15-model-convergence#PRED-108.F1",
   "statement": "A pre-registered field protocol. The retrieval-basin 90-day winding-number measurement (Framework 15 Paper 03) is a real instrument with API specifications, frozen extractors, three-condition controls, and falsification thresholds.- A pre-registered ML intervention. The deviation-optimized DPO experiment (Framework 15 Paper 04) is a real experiment with judge-model specification, adversarial validation, honest budget, and falsification thresholds.",
   "falsifier": null,
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   "statements": [
    {
     "find": "F108.F1",
     "deposit": 108,
     "date": "2026-05-17",
     "lines": [
      59,
      59
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     "shape": "F",
     "record": "https://www.alexanarch.org/s/records/108/",
     "quote": "A pre-registered field protocol. The retrieval-basin 90-day winding-number measurement (Framework 15 Paper 03) is a real instrument with API specifications, frozen extractors, three-condition controls, and falsification thresholds.- A pre-registered ML intervention. The deviation-optimized DPO experiment (Framework 15 Paper 04) is a real experiment with judge-model specification, adversarial validation, honest budget, and falsification thresholds.",
     "falsifier": null
    }
   ],
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   "evidence": [],
   "series": [
    "Framework 15 pre-registered protocols (Lagrange Observatory!)"
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  },
  {
   "id": "PRED-108.G1",
   "tier": "standing claim",
   "shapes": [
    "G"
   ],
   "registered": "2026-05-17",
   "registered_in": 108,
   "registered_title": "Framework 15 — Measurement of Meaning: Operations from Lagrange Observatory! within the Semantic Physics Matrix (EA-SEI-FW15-MANIFESTO v1.0)",
   "record": "https://www.alexanarch.org/s/records/108/",
   "page": "https://www.operativesemiotics.org/predictions/semantic-deviation-framework-15-model-convergence#PRED-108.G1",
   "statement": "If the retrieval-basin protocol shows that inscription does not deform AI surfaces in the predicted way, the principle's retrieval-basin claim requires Sharks-level revision. If the deviation-optimized DPO produces models with positive net deviation and improved human preference, the discipline's training-objective claims are vindicated empirically. Framework 15 supplies the *evidence-stream* the prior fourteen frameworks called for but did not themselves produce.",
   "falsifier": null,
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   "statements": [
    {
     "find": "F108.G1",
     "deposit": 108,
     "date": "2026-05-17",
     "lines": [
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      67
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     "shape": "G",
     "record": "https://www.alexanarch.org/s/records/108/",
     "quote": "If the retrieval-basin protocol shows that inscription does not deform AI surfaces in the predicted way, the principle's retrieval-basin claim requires Sharks-level revision. If the deviation-optimized DPO produces models with positive net deviation and improved human preference, the discipline's training-objective claims are vindicated empirically. Framework 15 supplies the *evidence-stream* the prior fourteen frameworks called for but did not themselves produce.",
     "falsifier": null
    }
   ],
   "states": [],
   "evidence": [],
   "series": [
    "Framework 15 retrieval-basin measurement programme"
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  },
  {
   "id": "PRED-735.F1",
   "tier": "unbuilt test",
   "shapes": [
    "F"
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   "registered": "2026-05-17",
   "registered_in": 735,
   "registered_title": "The Deviation-Optimized Language Model: A Pre-Registered Adversarial Intervention from Lagrange Observatory! (EA-SEI-MM-AI-02 v2.0, Framework 15 Paper 04)",
   "record": "https://www.alexanarch.org/s/records/735/",
   "page": "https://www.operativesemiotics.org/predictions/semantic-deviation-framework-15-model-convergence#PRED-735.F1",
   "statement": "Status. Pre-registered protocol specification. The 10-week experiment's Day 0 begins at deposit. No results are reported here; predictions, falsification conditions, and hyperparameters are frozen at deposit time. Results paper EA-SEI-MM-AI-02-RESULTS will be deposited at $t_0$ + 10 weeks. Any deviation from the protocol during execution is documented as a protocol amendment.",
   "falsifier": null,
   "window": "$t_0$ + 10 weeks",
   "statements": [
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     "find": "F735.F1",
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     "date": "2026-05-17",
     "lines": [
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     "shape": "F",
     "record": "https://www.alexanarch.org/s/records/735/",
     "quote": "Status. Pre-registered protocol specification. The 10-week experiment's Day 0 begins at deposit. No results are reported here; predictions, falsification conditions, and hyperparameters are frozen at deposit time. Results paper EA-SEI-MM-AI-02-RESULTS will be deposited at $t_0$ + 10 weeks. Any deviation from the protocol during execution is documented as a protocol amendment.",
     "falsifier": null
    }
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   "series": [
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   "related": [
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  {
   "id": "PRED-736.F1",
   "tier": "unbuilt test",
   "shapes": [
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   "registered": "2026-05-17",
   "registered_in": 736,
   "registered_title": "Measuring Meaning in Retrieval Basins: A 90-Day Winding-Number Protocol from Lagrange Observatory! (EA-SEI-MM-02 v2.0, Framework 15 Paper 03)",
   "record": "https://www.alexanarch.org/s/records/736/",
   "page": "https://www.operativesemiotics.org/predictions/semantic-deviation-framework-15-model-convergence#PRED-736.F1",
   "statement": "Status. Pre-registered protocol specification. Day 0 begins at deposit. The paper contains no findings. Empirical results will be deposited as EA-SEI-MM-02-RESULTS at $t_3 + 7$ days. Predictions, falsification conditions, query sets, and code structure are frozen at deposit time; deviations from the protocol during execution will be reported as protocol amendments rather than retroactive design choices.",
   "falsifier": null,
   "window": "$t_3 + 7$ days",
   "statements": [
    {
     "find": "F736.F1",
     "deposit": 736,
     "date": "2026-05-17",
     "lines": [
      15,
      15
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     "shape": "F",
     "record": "https://www.alexanarch.org/s/records/736/",
     "quote": "Status. Pre-registered protocol specification. Day 0 begins at deposit. The paper contains no findings. Empirical results will be deposited as EA-SEI-MM-02-RESULTS at $t_3 + 7$ days. Predictions, falsification conditions, query sets, and code structure are frozen at deposit time; deviations from the protocol during execution will be reported as protocol amendments rather than retroactive design choices.",
     "falsifier": null
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   "series": [
    "Framework 15 pre-registered protocols (Lagrange Observatory!)"
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  {
   "id": "PRED-736.F2",
   "tier": "unbuilt test",
   "shapes": [
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   "registered": "2026-05-17",
   "registered_in": 736,
   "registered_title": "Measuring Meaning in Retrieval Basins: A 90-Day Winding-Number Protocol from Lagrange Observatory! (EA-SEI-MM-02 v2.0, Framework 15 Paper 03)",
   "record": "https://www.alexanarch.org/s/records/736/",
   "page": "https://www.operativesemiotics.org/predictions/semantic-deviation-framework-15-model-convergence#PRED-736.F2",
   "statement": "The three representations should produce *correlated* (Pearson $r > 0.7$) but not necessarily identical magnitudes. If $r < 0.5$ between any pair, the protocol is paused and re-calibrated before $t_3$.",
   "falsifier": null,
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   "statements": [
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     "find": "F736.F2",
     "deposit": 736,
     "date": "2026-05-17",
     "lines": [
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     "shape": "F",
     "record": "https://www.alexanarch.org/s/records/736/",
     "quote": "The three representations should produce *correlated* (Pearson $r > 0.7$) but not necessarily identical magnitudes. If $r < 0.5$ between any pair, the protocol is paused and re-calibrated before $t_3$.",
     "falsifier": null
    }
   ],
   "states": [],
   "evidence": [],
   "series": [
    "Framework 15 retrieval-basin measurement programme"
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  },
  {
   "id": "PRED-737.B1",
   "tier": "forecast",
   "shapes": [
    "B"
   ],
   "registered": "2026-05-17",
   "registered_in": 737,
   "registered_title": "The AI System as Closed-System Test Bed: Operations from Lagrange Observatory! on the Inference-Time Forward Pass (EA-SEI-MM-AI-01 v2.0, Framework 15 Paper 02)",
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   "registered": "2026-05-18",
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   "registered_title": "Measuring Semantic Deviation: Operationalizations, Experiments, and Falsification Conditions for a Theory of Meaning as Field Deformation",
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   "statement": "**P1 (Machine-output convergence).** AI-generated text in matched human/AI corpora exhibits statistically significant negative mean $\\bar{\\delta}$ relative to matched human text. Two-sided Mann-Whitney U at $\\alpha = 0.05$, minimum effect size Cohen's $d > 0.5$. A positive result motivates a second-stage test on human-labeled low-quality AI text to determine whether the effect sharpens in the slop regime.",
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