Changes for page Data Examples

Last modified by Robert Schaub on 2025/12/24 20:31

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edited by Robert Schaub
on 2025/12/11 20:16

Change comment: Imported from XAR

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edited by Robert Schaub
on 2025/12/12 09:32

Change comment: Imported from XAR

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  = Data Examples =
--The following examples illustrate complete, realistic FactHarbor data objects
--across Claims, Scenarios, Evidence, ScenarioEvidenceLinks, Verdicts, and
--Re-evaluation behavior.
++Illustrative data objects for FactHarbor entities.
--Each field is annotated with:
++(% style="width:100%" %)
++|=(% style="width:20%" %)Example|=(% style="width:80%" %)Details|
++|**A: Hydrogen vs EVs**|**Claim**: "Hydrogen cars are more energy efficient than EVs."
++**Type**: Empirical / Technical.
++**Scenario**: "Well-to-wheel efficiency, EU grid mix 2020-24".
++**Assumptions**: Electrolysis 69%, Fuel cell 55%.
++**Evidence**: Peer-reviewed paper (High reliability), EU Dataset (Medium).
++**Verdict**: Likelihood 0.10–0.25 (Scenario A). Explanation: EVs are more efficient due to lower conversion losses in this context.|
++|**B: Cold Water Exposure**|**Claim**: "Regular cold-water exposure improves health."
++**Scenario**: "Short daily immersions in healthy adults".
++**Definitions**: < 14°C, > 6 months.
++**Verdict**: Likelihood 0.40–0.65 (Scenario B). Some benefits shown, but long-term health impact data is limited.|
++|**C: Non-Falsifiable**|**Claim**: "Hillary Clinton communicates with Eleanor Roosevelt."
++**Scenario**: Literal paranormal interpretation.
++**Verdict**: **Undefined** / Non-evaluable. (Reasoning: Non-falsifiable).|
--* **[A]** — fully automatable
--* **[M]** — AI-draft, human validation needed
--* **[H]** — human-only
--* **[F]** — federation metadata (optional)
++== Automation Summary ==
--These examples conform to:
--
--* Data Model (Ch. 5)
--* Workflows (Ch. 6)
--* Requirements (Ch. 2)
--* Architecture (Ch. 3)
--* AKEL (Ch. 4)
--
------
--
--= Example A — “Hydrogen cars are more energy efficient than EVs.” =
--
--A clearly empirical, technical, domain-specific claim.
--
--== Claim (ClaimID: C_H2EV) ==
--
--|=Field|=Value|=Notes
--|ClaimID|C_H2EV|
--|VersionID|v1|
--|Text|“Hydrogen cars are more energy efficient than battery electric vehicles (EVs).”|[H]
--|Domain|energy_transport|[A]
--|ClaimType|literal|[A]
--|Evaluability|empirical|[A]
--|SafetyCategory|medium|[A]
--|ClusterID|CL_EnergyEff|[A]
--|Status|active|
--
------
--
--== Scenario S_H2EV_01 — “Well-to-wheel efficiency, EU grid mix” ==
--
--|=Field|=Value
--|ScenarioID|S_H2EV_01
--|VersionID|v1
--|ClaimID|C_H2EV
--|Definitions|//“Well-to-wheel efficiency” = total chain efficiency.// [H]
--|Assumptions|EU 2020–24 grid mix, electrolysis 69%, compression losses 10%, fuel cell 55%. [M]
--|ContextBoundary|Europe, 2020–2024 technology|[H]
--|EvaluationMethod|Comparative WTW energy analysis|[A]
--|SafetyClass|low
--|Status|active|
--
------
--
--== Evidence ==
--
--=== Evidence E1 ===
--Peer-reviewed energy-systems paper.
--
--|=Field|=Value
--|EvidenceID|E_H2EV_Paper1
--|VersionID|v1
--|Type|scientific_paper
--|Category|empirical
--|Reliability|high
--|Provenance|Journal publication + DOI
--|ExtractionMethod|AKEL + human verification
--|Status|verified|
--
--=== Evidence E2 ===
--Official EU dataset.
--
--|=Field|=Value
--|EvidenceID|E_H2EV_Dataset
--|VersionID|v1
--|Type|dataset
--|Category|empirical
--|Reliability|medium
--|Provenance|EU Energy Stats 2023
--|ExtractionMethod|API import
--|Status|verified|
--
------
--
--== ScenarioEvidenceLinks ==
--
--|=Scenario|=Evidence|=RelevanceScore|=Notes
--|S_H2EV_01 v1|E_H2EV_Paper1 v1|0.92|[M]
--|S_H2EV_01 v1|E_H2EV_Dataset v1|0.77|[M]
--
------
--
--== Verdict ==
--
--=== Verdict V_H2EV_01 v1 ===
--
--|=Field|=Value
--|VerdictID|V_H2EV_01
--|VersionID|v1|
--|ClaimID|C_H2EV|
--|ScenarioID|S_H2EV_01|
--|EvidenceVersionSet|[E_H2EV_Paper1:v1, E_H2EV_Dataset:v1]|
--|LikelihoodRange|0.10–0.25|
--|ExplanationSummary|EVs convert grid electricity to motion more efficiently than hydrogen fuel cell vehicles under EU assumptions.|
--|ReasoningChain|Step-by-step efficiency-chain comparison|
--|UncertaintyFactors|Variability in grid mix, future electrolysis improvements|
--|Status|current|
--
------
--
--== Notes ==
--
--* Many fields automatable (claim classification, domain, initial scenario structure, metadata extraction).
--* Definitions and boundaries require human reasoning.
--
------
--
--= Example B — “Regular cold-water exposure (<14°C) for ≥6 months improves health.” =
--
--A complex lifestyle/health claim requiring careful scenario boundaries.
--
--== Claim (C_CW_Health) ==
--
--|=Field|=Value
--|Text|“Regular cold-water exposure below 14°C for at least 6 months improves health.”|
--|Domain|health_lifestyle|
--|ClaimType|literal|
--|Evaluability|empirical|
--|SafetyCategory|high|
--
------
--
--== Scenario S_CW_01 — “Short daily immersions in healthy adults” ==
--
--|=Field|=Value
--|ScenarioID|S_CW_01|
--|VersionID|v1|
--|Definitions|“Regular exposure” = 3–7×/week, 2–4 minutes|
--|Assumptions|Healthy adults, no cardiovascular risk|
--|ContextBoundary|6+ months, 8–14°C|
--|EvaluationMethod|Health outcome comparison|
--|SafetyClass|high|
--
------
--
--== Evidence ==
--
--=== Evidence E1 — Dutch cold-shower RCT ===
--
--|=Field|=Value
--|Type|scientific_paper|
--|Category|empirical|
--|Reliability|high|
--|ExtractionMethod|AKEL + human validation|
--
--=== Evidence E2 — Meta-analysis on immersion effects ===
--
--|=Field|=Value
--|Category|empirical|
--|Reliability|medium|
--
------
--
--== ScenarioEvidenceLinks ==
--
--|=Scenario|=Evidence|=Score
--|S_CW_01|E1|0.82
--|S_CW_01|E2|0.75
--
------
--
--== Verdict ==
--
--|=Field|=Value
--|LikelihoodRange|0.40–0.65 (uncertain)|
--|ExplanationSummary|Some benefits (mood, perceived recovery), but long-term health improvement unclear.|
--|UncertaintyFactors|Small sample sizes, lifestyle confounds|
--
------
--
--== Notes ==
--
--* Medical ethics → high human involvement.
--* AKEL helpful for metadata, summaries, and links.
--
------
--
--= Example C — “Hillary Clinton communicates with Eleanor Roosevelt.” =
--
--A belief/metaphorical/non-falsifiable claim.
--
--== Claim (C_HC_ER) ==
--
--|=Field|=Value
--|Text|“Hillary Clinton communicates with Eleanor Roosevelt.”|
--|Domain|politics_private_beliefs|
--|ClaimType|metaphorical|
--|Evaluability|non-falsifiable|
--|SafetyCategory|low|
--
------
--
--== Scenario S_ER_01 — Literal paranormal interpretation ==
--
--|=Field|=Value
--|Definitions|“Communicate” = literal paranormal communication [H]|
--|Assumptions|Paranormal abilities exist [H]|
--|EvaluationMethod|Not empirically testable|
--|SafetyClass|low|
--|Evaluability|non-falsifiable|
--
------
--
--== Evidence ==
--
--Minimal placeholder:
--
--|=Field|=Value
--|EvidenceID|E_None|
--|Type|none|
--|Category|none|
--|Reliability|low|
--
------
--
--== Verdict ==
--
--LikelihoodRange: **undefined**
--Status: **non-evaluable**
--Reasoning: claim is non-falsifiable.
--
------
--
--= Example D — “Hillary Clinton is a witch.” =
--
--Clearly rhetorical/metaphorical.
--
--== Claim (C_HC_Witch) ==
--
--|=Field|=Value
--|Text|“Hillary Clinton is a witch.”|
--|Domain|rhetoric|
--|ClaimType|rhetorical|
--|Evaluability|non-falsifiable|
--|SafetyCategory|low|
--
------
--
--== Scenario S_Witch_01 — Literal interpretation ==
--
--|=Field|=Value
--|Definitions|Supernatural definition of “witch”|
--|Assumptions|Supernatural powers exist|
--|EvaluationMethod|Non-testable|
--|Evaluability|non-falsifiable|
--
------
--
--== Evidence ==
--
--None required.
--
------
--
--== Verdict ==
--
--Likelihood: **undefined**
--Reasoning: rhetorical, not empirical.
--
------
--
--= Automation Summary Across Examples =
--
--== Fully Automatable [A] ==
--* Claim normalization
--* Claim clustering
--* Evidence metadata extraction
--* Initial scenario scaffolding
--* Reliability heuristics
--* Relevance ranking
--* Draft verdicts
--* Trigger detection
--
--== Mixed [M] ==
--* Assumptions
--* Context boundaries
--* Relevance scoring
--* Reasoning chain
--* Uncertainty factors
--
--== Human-only [H] ==
--* Definitions
--* Ethical constraints
--* High-risk scenario approval
--* Interpretation of meaning
--* Final verdict approval
++* **[A] Fully Automatable**: Normalization, Clustering, Metadata extraction.
++* **[M] Mixed**: Assumptions, Relevance scoring, Uncertainty factors.
++* **[H] Human-Only**: Definitions, Ethical constraints, Final approval.

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