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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
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).|
	7	+Each field is annotated with:
20	20
21		-== Automation Summary ==
	9	+* **[A]** — fully automatable
	10	+* **[M]** — AI-draft, human validation needed
	11	+* **[H]** — human-only
	12	+* **[F]** — federation metadata (optional)
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.
	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	+
	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	+
	43	+== Scenario S_H2EV_01 — “Well-to-wheel efficiency, EU grid mix” ==
	44	+
	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	+
	58	+== Evidence ==
	59	+
	60	+=== Evidence E1 ===
	61	+Peer-reviewed energy-systems paper.
	62	+
	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	+=== Evidence E2 ===
	74	+Official EU dataset.
	75	+
	76	+|=Field|=Value
	77	+|EvidenceID|E_H2EV_Dataset
	78	+|VersionID|v1
	79	+|Type|dataset
	80	+|Category|empirical
	81	+|Reliability|medium
	82	+|Provenance|EU Energy Stats 2023
	83	+|ExtractionMethod|API import
	84	+|Status|verified|
	85	+
	86	+----
	87	+
	88	+== ScenarioEvidenceLinks ==
	89	+
	90	+|=Scenario|=Evidence|=RelevanceScore|=Notes
	91	+|S_H2EV_01 v1|E_H2EV_Paper1 v1|0.92|[M]
	92	+|S_H2EV_01 v1|E_H2EV_Dataset v1|0.77|[M]
	93	+
	94	+----
	95	+
	96	+== Verdict ==
	97	+
	98	+=== Verdict V_H2EV_01 v1 ===
	99	+
	100	+|=Field|=Value
	101	+|VerdictID|V_H2EV_01
	102	+|VersionID|v1|
	103	+|ClaimID|C_H2EV|
	104	+|ScenarioID|S_H2EV_01|
	105	+|EvidenceVersionSet|[E_H2EV_Paper1:v1, E_H2EV_Dataset:v1]|
	106	+|LikelihoodRange|0.10–0.25|
	107	+|ExplanationSummary|EVs convert grid electricity to motion more efficiently than hydrogen fuel cell vehicles under EU assumptions.|
	108	+|ReasoningChain|Step-by-step efficiency-chain comparison|
	109	+|UncertaintyFactors|Variability in grid mix, future electrolysis improvements|
	110	+|Status|current|
	111	+
	112	+----
	113	+
	114	+== Notes ==
	115	+
	116	+* Many fields automatable (claim classification, domain, initial scenario structure, metadata extraction).
	117	+* Definitions and boundaries require human reasoning.
	118	+
	119	+----
	120	+
	121	+= Example B — “Regular cold-water exposure (<14°C) for ≥6 months improves health.” =
	122	+
	123	+A complex lifestyle/health claim requiring careful scenario boundaries.
	124	+
	125	+== Claim (C_CW_Health) ==
	126	+
	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	+
	136	+== Scenario S_CW_01 — “Short daily immersions in healthy adults” ==
	137	+
	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	+
	149	+== Evidence ==
	150	+
	151	+=== Evidence E1 — Dutch cold-shower RCT ===
	152	+
	153	+|=Field|=Value
	154	+|Type|scientific_paper|
	155	+|Category|empirical|
	156	+|Reliability|high|
	157	+|ExtractionMethod|AKEL + human validation|
	158	+
	159	+=== Evidence E2 — Meta-analysis on immersion effects ===
	160	+
	161	+|=Field|=Value
	162	+|Category|empirical|
	163	+|Reliability|medium|
	164	+
	165	+----
	166	+
	167	+== ScenarioEvidenceLinks ==
	168	+
	169	+|=Scenario|=Evidence|=Score
	170	+|S_CW_01|E1|0.82
	171	+|S_CW_01|E2|0.75
	172	+
	173	+----
	174	+
	175	+== Verdict ==
	176	+
	177	+|=Field|=Value
	178	+|LikelihoodRange|0.40–0.65 (uncertain)|
	179	+|ExplanationSummary|Some benefits (mood, perceived recovery), but long-term health improvement unclear.|
	180	+|UncertaintyFactors|Small sample sizes, lifestyle confounds|
	181	+
	182	+----
	183	+
	184	+== Notes ==
	185	+
	186	+* Medical ethics → high human involvement.
	187	+* AKEL helpful for metadata, summaries, and links.
	188	+
	189	+----
	190	+
	191	+= Example C — “Hillary Clinton communicates with Eleanor Roosevelt.” =
	192	+
	193	+A belief/metaphorical/non-falsifiable claim.
	194	+
	195	+== Claim (C_HC_ER) ==
	196	+
	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	+
	206	+== Scenario S_ER_01 — Literal paranormal interpretation ==
	207	+
	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	+
	217	+== Evidence ==
	218	+
	219	+Minimal placeholder:
	220	+
	221	+|=Field|=Value
	222	+|EvidenceID|E_None|
	223	+|Type|none|
	224	+|Category|none|
	225	+|Reliability|low|
	226	+
	227	+----
	228	+
	229	+== Verdict ==
	230	+
	231	+LikelihoodRange: **undefined**
	232	+Status: **non-evaluable**
	233	+Reasoning: claim is non-falsifiable.
	234	+
	235	+----
	236	+
	237	+= Example D — “Hillary Clinton is a witch.” =
	238	+
	239	+Clearly rhetorical/metaphorical.
	240	+
	241	+== Claim (C_HC_Witch) ==
	242	+
	243	+|=Field|=Value
	244	+|Text|“Hillary Clinton is a witch.”|
	245	+|Domain|rhetoric|
	246	+|ClaimType|rhetorical|
	247	+|Evaluability|non-falsifiable|
	248	+|SafetyCategory|low|
	249	+
	250	+----
	251	+
	252	+== Scenario S_Witch_01 — Literal interpretation ==
	253	+
	254	+|=Field|=Value
	255	+|Definitions|Supernatural definition of “witch”|
	256	+|Assumptions|Supernatural powers exist|
	257	+|EvaluationMethod|Non-testable|
	258	+|Evaluability|non-falsifiable|
	259	+
	260	+----
	261	+
	262	+== Evidence ==
	263	+
	264	+None required.
	265	+
	266	+----
	267	+
	268	+== Verdict ==
	269	+
	270	+Likelihood: **undefined**
	271	+Reasoning: rhetorical, not empirical.
	272	+
	273	+----
	274	+
	275	+= Automation Summary Across Examples =
	276	+
	277	+== Fully Automatable [A] ==
	278	+* Claim normalization
	279	+* Claim clustering
	280	+* Evidence metadata extraction
	281	+* Initial scenario scaffolding
	282	+* Reliability heuristics
	283	+* Relevance ranking
	284	+* Draft verdicts
	285	+* Trigger detection
	286	+
	287	+== Mixed [M] ==
	288	+* Assumptions
	289	+* Context boundaries
	290	+* Relevance scoring
	291	+* Reasoning chain
	292	+* Uncertainty factors
	293	+
	294	+== Human-only [H] ==
	295	+* Definitions
	296	+* Ethical constraints
	297	+* High-risk scenario approval
	298	+* Interpretation of meaning
	299	+* Final verdict approval