Changes for page Data Examples
Last modified by Robert Schaub on 2025/12/24 20:31
To version 6.2
edited by Robert Schaub
on 2025/12/16 20:25
on 2025/12/16 20:25
Change comment:
Update document after refactoring.
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... ... @@ -1,25 +1,316 @@ 1 1 = Data Examples = 2 2 3 -Illustrative data objects for FactHarbor entities. 3 +The following examples illustrate complete, realistic FactHarbor data objects 4 +across Claims, Scenarios, Evidence, ScenarioEvidenceLinks, Verdicts, and 5 +Re-evaluation behavior. 4 4 7 +Each field is annotated with: 8 + 9 +* **[A]** — fully automatable 10 +* **[M]** — AI-draft, human validation needed 11 +* **[H]** — human-only 12 +* **[F]** — federation metadata (optional) 13 + 14 +These examples conform to: 15 + 16 +* Data Model (Ch. 5) 17 +* Workflows (Ch. 6) 18 +* Requirements (Ch. 2) 19 +* Architecture (Ch. 3) 20 +* AKEL (Ch. 4) 21 + 22 +---- 23 + 24 += Example A — “Hydrogen cars are more energy efficient than EVs.” = 25 + 26 +A clearly empirical, technical, domain-specific claim. 27 + 28 +== Claim (ClaimID: C_H2EV) == 29 + 5 5 (% style="width:100%" %) 6 -|=(% style="width:20%" %)Example|=(% style="width:80%" %)Details| 7 -|**A: Hydrogen vs EVs**|**Claim**: "Hydrogen cars are more energy efficient than EVs." 8 -**Type**: Empirical / Technical. 9 -**Scenario**: "Well-to-wheel efficiency, EU grid mix 2020-24". 10 -**Assumptions**: Electrolysis 69%, Fuel cell 55%. 11 -**Evidence**: Peer-reviewed paper (High reliability), EU Dataset (Medium). 12 -**Verdict**: Likelihood 0.10–0.25 (Scenario A). Explanation: EVs are more efficient due to lower conversion losses in this context.| 13 -|**B: Cold Water Exposure**|**Claim**: "Regular cold-water exposure improves health." 14 -**Scenario**: "Short daily immersions in healthy adults". 15 -**Definitions**: < 14°C, > 6 months. 16 -**Verdict**: Likelihood 0.40–0.65 (Scenario B). Some benefits shown, but long-term health impact data is limited.| 17 -|**C: Non-Falsifiable**|**Claim**: "Hillary Clinton communicates with Eleanor Roosevelt." 18 -**Scenario**: Literal paranormal interpretation. 19 -**Verdict**: **Undefined** / Non-evaluable. (Reasoning: Non-falsifiable).| 31 +|=Field|=Value|=Notes 32 +|ClaimID|C_H2EV| 33 +|VersionID|v1| 34 +|Text|“Hydrogen cars are more energy efficient than battery electric vehicles (EVs).”|[H] 35 +|Domain|energy_transport|[A] 36 +|ClaimType|literal|[A] 37 +|Evaluability|empirical|[A] 38 +|SafetyCategory|medium|[A] 39 +|ClusterID|CL_EnergyEff|[A] 40 +|Status|active| 20 20 21 - == Automation Summary ==42 +---- 22 22 23 -* **[A] Fully Automatable**: Normalization, Clustering, Metadata extraction. 24 -* **[M] Mixed**: Assumptions, Relevance scoring, Uncertainty factors. 25 -* **[H] Human-Only**: Definitions, Ethical constraints, Final approval. 44 +== Scenario S_H2EV_01 — “Well-to-wheel efficiency, EU grid mix” == 45 + 46 +(% style="width:100%" %) 47 +|=Field|=Value 48 +|ScenarioID|S_H2EV_01 49 +|VersionID|v1 50 +|ClaimID|C_H2EV 51 +|Definitions|//“Well-to-wheel efficiency” = total chain efficiency.// [H] 52 +|Assumptions|EU 2020–24 grid mix, electrolysis 69%, compression losses 10%, fuel cell 55%. [M] 53 +|ContextBoundary|Europe, 2020–2024 technology|[H] 54 +|EvaluationMethod|Comparative WTW energy analysis|[A] 55 +|SafetyClass|low 56 +|Status|active| 57 + 58 +---- 59 + 60 +== Evidence == 61 + 62 +=== Evidence E1 === 63 +Peer-reviewed energy-systems paper. 64 + 65 +(% style="width:100%" %) 66 +|=Field|=Value 67 +|EvidenceID|E_H2EV_Paper1 68 +|VersionID|v1 69 +|Type|scientific_paper 70 +|Category|empirical 71 +|Reliability|high 72 +|Provenance|Journal publication + DOI 73 +|ExtractionMethod|AKEL + human verification 74 +|Status|verified| 75 + 76 +=== Evidence E2 === 77 +Official EU dataset. 78 + 79 +(% style="width:100%" %) 80 +|=Field|=Value 81 +|EvidenceID|E_H2EV_Dataset 82 +|VersionID|v1 83 +|Type|dataset 84 +|Category|empirical 85 +|Reliability|medium 86 +|Provenance|EU Energy Stats 2023 87 +|ExtractionMethod|API import 88 +|Status|verified| 89 + 90 +---- 91 + 92 +== ScenarioEvidenceLinks == 93 + 94 +(% style="width:100%" %) 95 +|=(% style="width:30%" %)Scenario|=(% style="width:40%" %)Evidence|=(% style="width:30%" %)RelevanceScore / Notes 96 +|S_H2EV_01 v1|E_H2EV_Paper1 v1|0.92 [M] 97 +|S_H2EV_01 v1|E_H2EV_Dataset v1|0.77 [M] 98 + 99 +---- 100 + 101 +== Verdict == 102 + 103 +=== Verdict V_H2EV_01 v1 === 104 + 105 +(% style="width:100%" %) 106 +|=Field|=Value 107 +|VerdictID|V_H2EV_01 108 +|VersionID|v1 109 +|ClaimID|C_H2EV 110 +|ScenarioID|S_H2EV_01 111 +|EvidenceVersionSet|[E_H2EV_Paper1:v1, E_H2EV_Dataset:v1] 112 +|LikelihoodRange|0.10–0.25| 113 +|ExplanationSummary|EVs convert grid electricity to motion more efficiently than hydrogen fuel cell vehicles under EU assumptions.| 114 +|ReasoningChain|Step-by-step efficiency-chain comparison| 115 +|UncertaintyFactors|Variability in grid mix, future electrolysis improvements| 116 +|Status|current| 117 + 118 +---- 119 + 120 +== Notes == 121 + 122 +* Many fields automatable (claim classification, domain, initial scenario structure, metadata extraction). 123 +* Definitions and boundaries require human reasoning. 124 + 125 +---- 126 + 127 += Example B — “Regular cold-water exposure (<14°C) for ≥6 months improves health.” = 128 + 129 +A complex lifestyle/health claim requiring careful scenario boundaries. 130 + 131 +== Claim (C_CW_Health) == 132 + 133 +(% style="width:100%" %) 134 +|=Field|=Value 135 +|Text|“Regular cold-water exposure below 14°C for at least 6 months improves health.”| 136 +|Domain|health_lifestyle| 137 +|ClaimType|literal| 138 +|Evaluability|empirical| 139 +|SafetyCategory|high| 140 + 141 +---- 142 + 143 +== Scenario S_CW_01 — “Short daily immersions in healthy adults” == 144 + 145 +(% style="width:100%" %) 146 +|=Field|=Value 147 +|ScenarioID|S_CW_01 148 +|VersionID|v1 149 +|Definitions|“Regular exposure” = 3–7×/week, 2–4 minutes| 150 +|Assumptions|Healthy adults, no cardiovascular risk| 151 +|ContextBoundary|6+ months, 8–14°C| 152 +|EvaluationMethod|Health outcome comparison| 153 +|SafetyClass|high| 154 + 155 +---- 156 + 157 +== Evidence == 158 + 159 +=== Evidence E1 — Dutch cold-shower RCT === 160 + 161 +(% style="width:100%" %) 162 +|=Field|=Value 163 +|Type|scientific_paper| 164 +|Category|empirical| 165 +|Reliability|high| 166 +|ExtractionMethod|AKEL + human validation| 167 + 168 +=== Evidence E2 — Meta-analysis on immersion effects === 169 + 170 +(% style="width:100%" %) 171 +|=Field|=Value 172 +|Category|empirical| 173 +|Reliability|medium| 174 + 175 +---- 176 + 177 +== ScenarioEvidenceLinks == 178 + 179 +(% style="width:100%" %) 180 +|=Scenario|=Evidence|=Score 181 +|S_CW_01|E1|0.82 182 +|S_CW_01|E2|0.75 183 + 184 +---- 185 + 186 +== Verdict == 187 + 188 +(% style="width:100%" %) 189 +|=Field|=Value 190 +|LikelihoodRange|0.40–0.65 (uncertain)| 191 +|ExplanationSummary|Some benefits (mood, perceived recovery), but long-term health improvement unclear.| 192 +|UncertaintyFactors|Small sample sizes, lifestyle confounds| 193 + 194 +---- 195 + 196 +== Notes == 197 + 198 +* Medical ethics → high human involvement. 199 +* AKEL helpful for metadata, summaries, and links. 200 + 201 +---- 202 + 203 += Example C — “Hillary Clinton communicates with Eleanor Roosevelt.” = 204 + 205 +A belief/metaphorical/non-falsifiable claim. 206 + 207 +== Claim (C_HC_ER) == 208 + 209 +(% style="width:100%" %) 210 +|=Field|=Value 211 +|Text|“Hillary Clinton communicates with Eleanor Roosevelt.”| 212 +|Domain|politics_private_beliefs| 213 +|ClaimType|metaphorical| 214 +|Evaluability|non-falsifiable| 215 +|SafetyCategory|low| 216 + 217 +---- 218 + 219 +== Scenario S_ER_01 — Literal paranormal interpretation == 220 + 221 +(% style="width:100%" %) 222 +|=Field|=Value 223 +|Definitions|“Communicate” = literal paranormal communication [H]| 224 +|Assumptions|Paranormal abilities exist [H]| 225 +|EvaluationMethod|Not empirically testable| 226 +|SafetyClass|low| 227 +|Evaluability|non-falsifiable| 228 + 229 +---- 230 + 231 +== Evidence == 232 + 233 +Minimal placeholder: 234 + 235 +(% style="width:100%" %) 236 +|=Field|=Value 237 +|EvidenceID|E_None| 238 +|Type|none| 239 +|Category|none| 240 +|Reliability|low| 241 + 242 +---- 243 + 244 +== Verdict == 245 + 246 +LikelihoodRange: **undefined** 247 +Status: **non-evaluable** 248 +Reasoning: claim is non-falsifiable. 249 + 250 +---- 251 + 252 += Example D — “Hillary Clinton is a witch.” = 253 + 254 +Clearly rhetorical/metaphorical. 255 + 256 +== Claim (C_HC_Witch) == 257 + 258 +(% style="width:100%" %) 259 +|=Field|=Value 260 +|Text|“Hillary Clinton is a witch.”| 261 +|Domain|rhetoric| 262 +|ClaimType|rhetorical| 263 +|Evaluability|non-falsifiable| 264 +|SafetyCategory|low| 265 + 266 +---- 267 + 268 +== Scenario S_Witch_01 — Literal interpretation == 269 + 270 +(% style="width:100%" %) 271 +|=Field|=Value 272 +|Definitions|Supernatural definition of “witch”| 273 +|Assumptions|Supernatural powers exist| 274 +|EvaluationMethod|Non-testable| 275 +|Evaluability|non-falsifiable| 276 + 277 +---- 278 + 279 +== Evidence == 280 + 281 +None required. 282 + 283 +---- 284 + 285 +== Verdict == 286 + 287 +Likelihood: **undefined** 288 +Reasoning: rhetorical, not empirical. 289 + 290 +---- 291 + 292 += Automation Summary Across Examples = 293 + 294 +== Fully Automatable [A] == 295 +* Claim normalization 296 +* Claim clustering 297 +* Evidence metadata extraction 298 +* Initial scenario scaffolding 299 +* Reliability heuristics 300 +* Relevance ranking 301 +* Draft verdicts 302 +* Trigger detection 303 + 304 +== Mixed [M] == 305 +* Assumptions 306 +* Context boundaries 307 +* Relevance scoring 308 +* Reasoning chain 309 +* Uncertainty factors 310 + 311 +== Human-only [H] == 312 +* Definitions 313 +* Ethical constraints 314 +* High-risk scenario approval 315 +* Interpretation of meaning 316 +* Final verdict approval