Yolia EnginesEvidence EngineOrigin: Yolia's safe educational catalogExact route: /biblioteca?ruta=motores/engine_evidence FULL TOPICEvidence Engine Name and classification Evidence Engine. Yolia Engine that distinguishes between a measured data point, a declared one, a regional estimate, an inference, and a scientific rule, a central topic in Yolia's complete catalog of Engines in Yolia's Library, within Yolia's complete catalog of Engines. What it is and what function it serves It is the engine that distinguishes between something measured, something stated by the user, a regional estimate, an inference, and a scientific rule, relating each statement to its provenance, scope, and review level within the case, before its result is combined with that of other engines. What this engine observes It observes each datum's source type, its declared scope, the method used to obtain it, its origin date, its usage license, and its review status, integrating this information before accepting a datum as reliable, information that later gets cross-checked with that of other engines. Why it matters within the complete process It is key because unverified data must not silently become facts within the system's reasoning, this engine being what keeps the real provenance of every claim used in a case visible, without this step the rest of the analysis would lose precision. Easy explanation Yolia distinguishes between something measured, something stated by the user, a regional estimate, an inference, and a scientific rule, linking each claim to its provenance, scope, and review level within the analyzed case, explained in simple language for anyone new to the topic. Medium explanation It records the source, species or context, method used, date of origin, usage license, review status, and application limits of each data point; unverified data does not silently become fact within the system, with more technical detail than the previous introductory level. Advanced explanation It keeps a ledger of evidence with each source's identity, integrity, taxonomic scope, and applicability; a general reference does not authorize automatic extrapolation nor a dose without further verification for the specific case, with the most technical level of detail available in this sheet. Risks it monitors It watches for claims without sufficient support, extrapolations outside a source's original context, and evidence that has become outdated over time, three signals this engine flags before accepting a piece of data as reliable, vigilance that helps avoid premature or unsafe decisions. Declared limits and warnings It states that the quality of available evidence can be uneven across different topics, and that a source's existence doesn't automatically guarantee its applicability to the specific case currently being analyzed, a limit openly declared to avoid false certainties. Which other engines it interacts with It interacts with practically every other engine, since each one depends on data whose provenance this engine verifies before it is used to calculate risk, compatibility, dosage, or any other conclusion for the case, a connection relevant within the system's overall functioning. Relevant differences between catalog species The principle of verifying provenance is identical for the catalog's 4 species; what changes is which specific sources are relevant according to the particular species and context of each case analyzed by the system, a difference that can change how the result is interpreted. How it is observed in practice on a case It is seen in the case explanation, allowing the origin of each piece of data used to be opened up and showing which part of that information is still pending full verification before the system treats it as a fully confirmed fact, something reflected in the final explanation delivered to the user. What happens if its signals are ignored If this engine is ignored, a regional estimate could be silently treated as a direct on-site measurement, inflating the system's confidence in data that in reality still requires additional verification for the case, a consequence the system seeks to avoid through this control. How Yolia uses it within the process Yolia uses it as a cross-cutting data-quality guardian, ensuring that every engine in the system knows how to distinguish between what is measured, what is declared, what is estimated, and what is inferred before drawing a conclusion from that information, part of the structured process the system follows in every case. Common interpretation errors Thinking that a general scientific source automatically applies to any particular species or context — the engine states that applicability must be verified case by case before accepting that extrapolation as valid, a common confusion worth clarifying about this engine. Evidence and limits of current knowledge Its specification remains as STRUCTURAL_SPECIFICATION, with room to expand the evidence ledger as more documented, reviewed sources accumulate within Yolia's complete knowledge system, with room for the specification to keep being refined. Related topics and engines Yolia Final Auditor, engine that reviews this evidence's coherence — Risk Engine, related engine — Evidence and uncertainty, related topic within Yolia's complete catalog, also see the directly related engines and modules.