Source identity
| Field | Recorded value |
|---|---|
| Source type | GitHub source |
| Original record | Janady13/AI_Brain / daemon/datasets/statistical_reasoning.json |
| Date | Not specified in source |
| Repository | Janady13/AI_Brain |
| Revision | 11aa50ef5d7692e8abd1b8d74ae31c5c29201b14 |
| Branch | codex/build-online-github-ani-irqa7y |
| Path or archive member | daemon/datasets/statistical_reasoning.json |
| Original SHA-256 | 05d5d26e3fce1a9a4551c7aa8b3b72c722834135db00136f3ff3f2ce07422ead |
| Git blob SHA | c1821454540ff7c4d5eb1c9f9407841026e672a4 |
| Reading-copy SHA-256 | 05d5d26e3fce1a9a4551c7aa8b3b72c722834135db00136f3ff3f2ce07422ead |
Every recorded occurrence
These 17 records contain identical original source bytes. Their paths and revisions remain part of the archive.
| Original record | Branch or archive | Revision / identifier | Original location |
|---|---|---|---|
| Janady13/AI_Brain / daemon/datasets/statistical_reasoning.json | codex/build-online-github-ani-irqa7y | 11aa50ef5d7692e8abd1b8d74ae31c5c29201b14 | Open original |
| Janady13/AI_Brain / daemon/datasets/statistical_reasoning.json | codex/build-online-github-ani-i3cigl | 3a6e002311c48c27ffd41ef4a2a31c63947128cd | Open original |
| Janady13/AI_Brain / daemon/datasets/statistical_reasoning.json | codex/build-online-github-ani-hrtsoc | 50285aff347685f043e7a72e4d72891eebaa6cd6 | Open original |
| Janady13/AI_Brain / daemon/datasets/statistical_reasoning.json | codex/build-online-github-ani-7v4kug | 5cd0bbf744b0aee9445913337e852889d9a8098e | Open original |
| Janady13/AI_Brain / daemon/datasets/statistical_reasoning.json | copilot/convert-python-files-to-csharp | 6d0767aad08781eb5c4e1eb14a42ec92c437be78 | Open original |
| Janady13/AI_Brain / daemon/datasets/statistical_reasoning.json | copilot/vscode1760283895982 | 9e1c1a5b171addb5bb4a83b2ea7dede50c1cc81e | Open original |
| Janady13/AI_Brain / daemon/datasets/statistical_reasoning.json | feature/seeder-engine-tests | cdb497ece2d1a770ca8c83eb1de8923cfaa9eceb | Open original |
| Janady13/AI_Brain / daemon/datasets/statistical_reasoning.json | codex/build-online-github-ani-7nmb21 | d0f50071c85cddaaebbf9c4dd1313bd7676986da | Open original |
| Janady13/AI_Brain / daemon/datasets/statistical_reasoning.json | codex/build-online-github-ani-0etkns | e250b19d446bdffd542c9eea31cead12e30800d6 | Open original |
| Janady13/AI_Brain / daemon/datasets/statistical_reasoning.json | codex/build-online-github-ani-irqa7y | 11aa50ef5d7692e8abd1b8d74ae31c5c29201b14 | Open original |
| Janady13/AI_Brain / daemon/datasets/statistical_reasoning.json | codex/build-online-github-ani-i3cigl | 3a6e002311c48c27ffd41ef4a2a31c63947128cd | Open original |
| Janady13/AI_Brain / daemon/datasets/statistical_reasoning.json | copilot/convert-python-files-to-csharp | 6d0767aad08781eb5c4e1eb14a42ec92c437be78 | Open original |
| Janady13/AI_Brain / daemon/datasets/statistical_reasoning.json | codex/build-online-github-ani-7v4kug | 5cd0bbf744b0aee9445913337e852889d9a8098e | Open original |
| Janady13/AI_Brain / daemon/datasets/statistical_reasoning.json | copilot/vscode1760283895982 | 9e1c1a5b171addb5bb4a83b2ea7dede50c1cc81e | Open original |
| Janady13/AI_Brain / daemon/datasets/statistical_reasoning.json | codex/build-online-github-ani-7nmb21 | d0f50071c85cddaaebbf9c4dd1313bd7676986da | Open original |
| Janady13/AI_Brain / daemon/datasets/statistical_reasoning.json | feature/seeder-engine-tests | cdb497ece2d1a770ca8c83eb1de8923cfaa9eceb | Open original |
| Janady13/AI_Brain / daemon/datasets/statistical_reasoning.json | codex/build-online-github-ani-hrtsoc | 50285aff347685f043e7a72e4d72891eebaa6cd6 | Open original |
Recovered source transcript
Download complete source text ↓99 lines · 2.6 KB
[
{
"feature": "email nurture",
"feature_type": "channel",
"mean_roi": 2.6,
"std_roi": 0.4,
"mean_ctr": 0.045,
"std_ctr": 0.012,
"sample_size": 185,
"weight": 0.85,
"baseline_budget": 18000,
"notes": "Lifecycle email programs with automated segmentation deliver resilient ROI across B2B SaaS cohorts."
},
{
"feature": "paid social",
"feature_type": "channel",
"mean_roi": 2.1,
"std_roi": 0.6,
"mean_ctr": 0.035,
"std_ctr": 0.015,
"sample_size": 240,
"weight": 0.8,
"baseline_budget": 22000,
"notes": "Paid social experimentation pairs creative testing with intent overlays for balanced CTR lift."
},
{
"feature": "enterprise",
"feature_type": "audience",
"mean_roi": 2.4,
"std_roi": 0.5,
"mean_ctr": 0.028,
"std_ctr": 0.009,
"sample_size": 160,
"weight": 0.9,
"baseline_budget": 32000,
"notes": "Enterprise cohorts convert with multi-threaded nurture and executive enablement."
},
{
"feature": "startup",
"feature_type": "audience",
"mean_roi": 2.0,
"std_roi": 0.55,
"mean_ctr": 0.04,
"std_ctr": 0.013,
"sample_size": 140,
"weight": 0.75,
"baseline_budget": 15000,
"notes": "Startup founders reward scrappy experiments and rapid payback cycles."
},
{
"feature": "conversion",
"feature_type": "objective",
"mean_roi": 2.8,
"std_roi": 0.45,
"mean_ctr": 0.05,
"std_ctr": 0.01,
"sample_size": 210,
"weight": 0.92,
"baseline_budget": 26000,
"notes": "Conversion-focused programs benefit from high-intent sequencing and social proof."
},
{
"feature": "retention",
"feature_type": "objective",
"mean_roi": 2.3,
"std_roi": 0.35,
"mean_ctr": 0.03,
"std_ctr": 0.008,
"sample_size": 130,
"weight": 0.7,
"baseline_budget": 20000,
"notes": "Retention programs lean on product education and lifecycle personalization."
},
{
"feature": "executive roundtable",
"feature_type": "offer",
"mean_roi": 3.1,
"std_roi": 0.5,
"mean_ctr": 0.025,
"std_ctr": 0.007,
"sample_size": 95,
"weight": 0.88,
"baseline_budget": 28000,
"notes": "Executive roundtables compound ROI when paired with ABM nurtures and analyst proof."
},
{
"feature": "interactive ROI calculator",
"feature_type": "offer",
"mean_roi": 2.5,
"std_roi": 0.42,
"mean_ctr": 0.048,
"std_ctr": 0.011,
"sample_size": 150,
"weight": 0.86,
"baseline_budget": 21000,
"notes": "Interactive ROI tools compress consideration by quantifying value early in the journey."
}
]