DATAFRUCTUS ALGORITHMICA — TEST RESULTS 09 Species: Datafructus algorithmica (The Data Fruit) Creator: Wagg · Owner: Family Tree Trust · Distributor: CTW, LLC Test Date: 2026-09-25 Test Type: Reproducibility Audit PURPOSE OF TEST To prove the species produces identical results on identical inputs. A buyer must be able to trust that what they see is what they get — not a one-time fluke. METHOD Ran the core loop 10 times with the same random seed (42) and the same input parameters. Measured the recovery correlation on each run. If the species is deterministic, all 10 runs produce the exact same number. RESULTS | Run | Recovery Correlation | | 1 | 0.967803 | | 2 | 0.967803 | | 3 | 0.967803 | | 4 | 0.967803 | | 5 | 0.967803 | | 6 | 0.967803 | | 7 | 0.967803 | | 8 | 0.967803 | | 9 | 0.967803 | | 10 | 0.967803 | Unique results across 10 runs: 1 RESULT PASS — perfectly reproducible. WHAT THIS PROVES 1. The species is deterministic. Given the same input, it always produces the same output. 2. Results are not random or one-time flukes. 3. Anyone who runs this test will get the same numbers. HONEST BOUNDARY This test proves reproducibility of the core loop with a fixed seed. It does not prove reproducibility across different hardware or operating systems. A future cross-platform test would confirm the species behaves identically everywhere. OUTPUT ARTIFACT Results file: test_09_reproducibility.txt Script: scripts/test_09_reproducibility.py © 2026 Family Tree Trust. All rights reserved. Distributed by CTW, LLC.

DATAFRUCTUS ALGORITHMICA — TEST RESULTS 10 Species: Datafructus algorithmica (The Data Fruit) Creator: Wagg · Owner: Family Tree Trust · Distributor: CTW, LLC Test Date: 2026-09-25 Test Type: Cross-Platform Validation Document Type: Full Technical Record PURPOSE OF TEST To determine whether the species produces identical output on any hardware platform, or whether it is tied to the server it was built on. If two independent machines generate the same fingerprint, portability is proven. METHOD A pure-Python implementation of the core loop was created. It uses no external libraries — only math and hashlib from the standard library. This guarantees the code will run on any device with a Python interpreter, from a server to a phone. CORE LOOP COMPONENTS 1. Deterministic noise generator (LCG) A linear congruential generator replaces NumPy random. Parameters: - Multiplier (a) = 1103515245 - Increment (c) = 12345 - Modulus (m) = 2^31 - Seed = 42 - Output range: -1 to 1 2. Signal generation Two sine waves combined into one hidden pattern: - Wave A: frequency 0.05 - Wave B: frequency 0.115 - Combination: Wave A + (0.5 x Wave B) - Sample count: N = 500 3. Noisy input The true pattern plus the LCG noise, element by element. 4. Pattern recovery A moving average with window size 25, computed by hand without NumPy. 5. Correlation scoring Pearson correlation computed manually — no SciPy, no NumPy. 6. Fingerprinting The recovery score is rounded to 10 decimal places, converted to a string, and hashed with SHA256. The hash is the fingerprint. RESULTS — DEVICE 1 | Field | Value | | Platform | Linux-6.8.0-137-generic-x86_64-with-glibc2.39 | | Host | Diamond Server (Ubuntu, DigitalOcean) | | Python | 3.12.3 | | Core loop result | 0.9796186078 | | Fingerprint | 93a376459af780835f3e77e6730b4c19006eb7d8150d27d4302bb96971bceec1 | RESULTS — DEVICE 2 | Field | Value | | Platform | Linux-6.6.122+-x86_64-with-glibc2.39 | | Host | Google Colab (cloud, separate infrastructure) | | Python | 3.13.15 | | Core loop result | 0.9796186078 | | Fingerprint | 93a376459af780835f3e77e6730b4c19006eb7d8150d27d4302bb96971bceec1 | RESULT PASS — Fingerprints match across two independent machines. Both devices produced: - Identical recovery score: 0.9796186078 - Identical SHA256 fingerprint: 93a376...bceec1 Two different operating system kernels (6.8.0 vs 6.6.122+), two different Python versions (3.12.3 vs 3.13.15), two entirely different machines — same result down to 10 decimal places. WHAT THIS PROVES 1. The species is portable. It is not tied to the server it was built on. 2. It produces identical results on independent hardware. 3. The deterministic design holds in practice, not just in theory. 4. A buyer running the species on their own hardware will get the result the page promises. HONEST BOUNDARY Both test devices ran Linux. The test proves portability across machines and Python versions, but not across operating systems. A Windows or macOS test would be required to confirm full platform independence. The test also measured a simplified pure-Python version of the core loop, not the full eight-layer pipeline. It confirms the deterministic foundation, not every layer individually. TECHNICAL NOTE ON DETERMINISM The result was guaranteed to be deterministic by design — the LCG is pure arithmetic, the moving average is pure arithmetic, and the correlation is pure arithmetic. No floating-point library differences could affect the result at this scale. The test confirms that this design choice holds when executed on different machines. This is not a difficult test to pass. It is a confirmation that a design decision was correctly implemented. That distinction matters for the honest record. OUTPUT ARTIFACT Script: scripts/test_10_crossplatform.py Full plain-Python version, no external dependencies, runs on any device with Python 3. © 2026 Family Tree Trust. All rights reserved. Distributed by CTW, LLC.