OpenAI GPT-5.6 Sol
Used for structured data extraction and graph resolution.
RDAs are not designed to be optimal; they are merely minimum safety standards established to prevent overt deficiency diseases (like scurvy or rickets) in the average, healthy population.
The original formulas were often based on the needs of young, healthy males, failing to account for the vast biochemical individuality of the broader population.
Standard allowances fail to factor in increased nutritional demands caused by:
While an RDA might keep you alive, achieving optimal health, high energy, and disease prevention almost always requires targeted, individualized supplementation above these baseline levels.
True nutritional balance depends on comprehensive testing (such as hair mineral analysis and blood work) to determine your specific cellular needs, rather than relying on a "one-size-fits-all" government guideline.
Daily diary ->
-> Recommended Dietary Allowance
Adults ->
-> Adults
A source-grounded pipeline turns Acu-Cell pages into a map of entities and claims. The graph organizes what the source says; it does not invent a medical answer or certify that a claim is correct.
The source pages are converted into clean, ordered text. Section context is retained so a sentence is interpreted with the heading and surrounding material that give it meaning.
A schema-constrained language model reads one complete source record at a time and identifies entities, directed relationships, endpoint roles, qualifiers and a rationale. The extraction rules prohibit creating a relationship from proximity or formatting alone.
Dose, timing, certainty, evidence and other conditions remain qualifiers on the precise relationship they modify. General facts about an entity become attributes. This prevents a qualified statement from being displayed as an unconditional one.
Name similarity, shared attributes, explicit identity statements and mutual semantic similarity produce 784 candidate groups. This stage only nominates candidates; it cannot merge them.
Each candidate group is judged against its source evidence. Names merge only when the supplied records establish exact identity, not merely because the terms seem related. The build merged 193 groups and absorbed 209 duplicate entity records; uncertain cases remain separate.
Relationship names are combined only when they are interchangeable. Broader and narrower meanings stay distinct. Resolved entity names are then applied to every claim, duplicate edges are consolidated without erasing qualifier differences, and the searchable graph is produced.
Used for structured data extraction and graph resolution.
Used under human direction to consolidate duplicate entities, validate the extracted data, and correct errors.
The source states that high levels of calcium can slow healing.
The condition stays on the relationship. It does not become a separate entity called “high calcium,” and it is not discarded.
The graph is an automated interpretation of source material, not an independent scientific review. Extraction can miss context, choose the wrong direction, overstate a relationship or fail to recognize two names as the same thing. The source itself may also be incomplete, disputed or outdated. Use the rationale and source links to inspect important claims directly.