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Wiki source code of Data Examples

Last modified by Robert Schaub on 2025/12/24 20:34
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Robert Schaub 1.1 1 = Data Examples =
2
3 The following examples illustrate complete, realistic FactHarbor data objects
4 across Claims, Scenarios, Evidence, ScenarioEvidenceLinks, Verdicts, and
5 Re-evaluation behavior.
6
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 = Example A — “Hydrogen cars are more energy efficient than EVs.” =
24
25 A clearly empirical, technical, domain-specific claim.
26
27 == 1. Claim (ClaimID: C_H2EV) ==
28
29 (% style="width:100%" %)
30 |=Field|=Value|=Notes
31 |ClaimID|C_H2EV|
32 |VersionID|v1|
33 |Text|“Hydrogen cars are more energy efficient than battery electric vehicles (EVs).”|[H]
34 |Domain|energy_transport|[A]
35 |ClaimType|literal|[A]
36 |Evaluability|empirical|[A]
37 |SafetyCategory|medium|[A]
38 |ClusterID|CL_EnergyEff|[A]
39 |Status|active|
40
41
42 == 2. Scenario S_H2EV_01 — “Well-to-wheel efficiency, EU grid mix” ==
43
44 (% style="width:100%" %)
45 |=Field|=Value
46 |ScenarioID|S_H2EV_01
47 |VersionID|v1
48 |ClaimID|C_H2EV
49 |Definitions|//“Well-to-wheel efficiency” = total chain efficiency.// [H]
50 |Assumptions|EU 2020–24 grid mix, electrolysis 69%, compression losses 10%, fuel cell 55%. [M]
51 |ContextBoundary|Europe, 2020–2024 technology|[H]
52 |EvaluationMethod|Comparative WTW energy analysis|[A]
53 |SafetyClass|low
54 |Status|active|
55
56
57 == 3. Evidence ==
58
59 === 3.1 Evidence E1 ===
60 Peer-reviewed energy-systems paper.
61
62 (% style="width:100%" %)
63 |=Field|=Value
64 |EvidenceID|E_H2EV_Paper1
65 |VersionID|v1
66 |Type|scientific_paper
67 |Category|empirical
68 |Reliability|high
69 |Provenance|Journal publication + DOI
70 |ExtractionMethod|AKEL + human verification
71 |Status|verified|
72
73 === 3.2 Evidence E2 ===
74 Official EU dataset.
75
76 (% style="width:100%" %)
77 |=Field|=Value
78 |EvidenceID|E_H2EV_Dataset
79 |VersionID|v1
80 |Type|dataset
81 |Category|empirical
82 |Reliability|medium
83 |Provenance|EU Energy Stats 2023
84 |ExtractionMethod|API import
85 |Status|verified|
86
87
88 == 4. ScenarioEvidenceLinks ==
89
90 (% style="width:100%" %)
91 |=(% style="width:30%" %)Scenario|=(% style="width:40%" %)Evidence|=(% style="width:30%" %)RelevanceScore / Notes
92 |S_H2EV_01 v1|E_H2EV_Paper1 v1|0.92 [M]
93 |S_H2EV_01 v1|E_H2EV_Dataset v1|0.77 [M]
94
95
96 == 5. Verdict ==
97
98 === 5.1 Verdict V_H2EV_01 v1 ===
99
100 (% style="width:100%" %)
101 |=Field|=Value
102 |VerdictID|V_H2EV_01
103 |VersionID|v1
104 |ClaimID|C_H2EV
105 |ScenarioID|S_H2EV_01
106 |EvidenceVersionSet|[E_H2EV_Paper1:v1, E_H2EV_Dataset:v1]
107 |LikelihoodRange|0.10–0.25|
108 |ExplanationSummary|EVs convert grid electricity to motion more efficiently than hydrogen fuel cell vehicles under EU assumptions.|
109 |ReasoningChain|Step-by-step efficiency-chain comparison|
110 |UncertaintyFactors|Variability in grid mix, future electrolysis improvements|
111 |Status|current|
112
113
114 == 6. Notes ==
115
116 * Many fields automatable (claim classification, domain, initial scenario structure, metadata extraction).
117 * Definitions and boundaries require human reasoning.
118
119
120 = Example B — “Regular cold-water exposure (<14°C) for ≥6 months improves health.” =
121
122 A complex lifestyle/health claim requiring careful scenario boundaries.
123
124 == 7. Claim (C_CW_Health) ==
125
126 (% style="width:100%" %)
127 |=Field|=Value
128 |Text|“Regular cold-water exposure below 14°C for at least 6 months improves health.”|
129 |Domain|health_lifestyle|
130 |ClaimType|literal|
131 |Evaluability|empirical|
132 |SafetyCategory|high|
133
134
135 == 8. Scenario S_CW_01 — “Short daily immersions in healthy adults” ==
136
137 (% style="width:100%" %)
138 |=Field|=Value
139 |ScenarioID|S_CW_01
140 |VersionID|v1
141 |Definitions|“Regular exposure” = 3–7×/week, 2–4 minutes|
142 |Assumptions|Healthy adults, no cardiovascular risk|
143 |ContextBoundary|6+ months, 8–14°C|
144 |EvaluationMethod|Health outcome comparison|
145 |SafetyClass|high|
146
147
148 == 9. Evidence ==
149
150 === 9.1 Evidence E1 — Dutch cold-shower RCT ===
151
152 (% style="width:100%" %)
153 |=Field|=Value
154 |Type|scientific_paper|
155 |Category|empirical|
156 |Reliability|high|
157 |ExtractionMethod|AKEL + human validation|
158
159 === 9.2 Evidence E2 — Meta-analysis on immersion effects ===
160
161 (% style="width:100%" %)
162 |=Field|=Value
163 |Category|empirical|
164 |Reliability|medium|
165
166
167 == 10. ScenarioEvidenceLinks ==
168
169 (% style="width:100%" %)
170 |=Scenario|=Evidence|=Score
171 |S_CW_01|E1|0.82
172 |S_CW_01|E2|0.75
173
174
175 == 11. Verdict ==
176
177 (% style="width:100%" %)
178 |=Field|=Value
179 |LikelihoodRange|0.40–0.65 (uncertain)|
180 |ExplanationSummary|Some benefits (mood, perceived recovery), but long-term health improvement unclear.|
181 |UncertaintyFactors|Small sample sizes, lifestyle confounds|
182
183
184 == 12. Notes ==
185
186 * Medical ethics → high human involvement.
187 * AKEL helpful for metadata, summaries, and links.
188
189
190 = Example C — “Hillary Clinton communicates with Eleanor Roosevelt.” =
191
192 A belief/metaphorical/non-falsifiable claim.
193
194 == 13. Claim (C_HC_ER) ==
195
196 (% style="width:100%" %)
197 |=Field|=Value
198 |Text|“Hillary Clinton communicates with Eleanor Roosevelt.”|
199 |Domain|politics_private_beliefs|
200 |ClaimType|metaphorical|
201 |Evaluability|non-falsifiable|
202 |SafetyCategory|low|
203
204
205 == 14. Scenario S_ER_01 — Literal paranormal interpretation ==
206
207 (% style="width:100%" %)
208 |=Field|=Value
209 |Definitions|“Communicate” = literal paranormal communication [H]|
210 |Assumptions|Paranormal abilities exist [H]|
211 |EvaluationMethod|Not empirically testable|
212 |SafetyClass|low|
213 |Evaluability|non-falsifiable|
214
215
216 == 15. Evidence ==
217
218 Minimal placeholder:
219
220 (% style="width:100%" %)
221 |=Field|=Value
222 |EvidenceID|E_None|
223 |Type|none|
224 |Category|none|
225 |Reliability|low|
226
227
228 == 16. Verdict ==
229
230 LikelihoodRange: **undefined**
231 Status: **non-evaluable**
232 Reasoning: claim is non-falsifiable.
233
234
235 = Example D — “Hillary Clinton is a witch.” =
236
237 Clearly rhetorical/metaphorical.
238
239 == 17. Claim (C_HC_Witch) ==
240
241 (% style="width:100%" %)
242 |=Field|=Value
243 |Text|“Hillary Clinton is a witch.”|
244 |Domain|rhetoric|
245 |ClaimType|rhetorical|
246 |Evaluability|non-falsifiable|
247 |SafetyCategory|low|
248
249
250 == 18. Scenario S_Witch_01 — Literal interpretation ==
251
252 (% style="width:100%" %)
253 |=Field|=Value
254 |Definitions|Supernatural definition of “witch”|
255 |Assumptions|Supernatural powers exist|
256 |EvaluationMethod|Non-testable|
257 |Evaluability|non-falsifiable|
258
259
260 == 19. Evidence ==
261
262 None required.
263
264
265 == 20. Verdict ==
266
267 Likelihood: **undefined**
268 Reasoning: rhetorical, not empirical.
269
270
271 = Automation Summary Across Examples =
272
273 == 21. Fully Automatable [A] ==
274 * Claim normalization
275 * Claim clustering
276 * Evidence metadata extraction
277 * Initial scenario scaffolding
278 * Reliability heuristics
279 * Relevance ranking
280 * Draft verdicts
281 * Trigger detection
282
283 == 22. Mixed [M] ==
284 * Assumptions
285 * Context boundaries
286 * Relevance scoring
287 * Reasoning chain
288 * Uncertainty factors
289
290 == 23. Human-only [H] ==
291 * Definitions
292 * Ethical constraints
293 * High-risk scenario approval
294 * Interpretation of meaning
295 * Final verdict approval