Records: annotated ledger surfaces and marginal interpretation
The records page describes how qizetsave treats each ledger page as an interpretive surface. Annotations are attached to lines, columns, and margins to record the semantic properties of numbers. Each annotation is concise: it includes a primitive type, a scope indicator, a provenance pointer, and a revision token. The presentation prioritizes legibility by keeping primary figures visually prominent while placing annotation markers in reserved marginal zones or adjacent micro-areas. Margin markers use minimal glyphs and short codes; in-line markers are brief and link to a compact legend that accompanies the record. The goal is descriptive documentation of representation so that readers can resolve how constraints and distributions are expressed within the record without requiring evaluative language.
Annotated record anatomy
An annotated record is composed of primary figures, marginal primitives, inline descriptors, and a compact legend. Primary figures occupy the central grid and maintain typographic precedence. Marginal primitives attach to rows or column bands and consist of a glyph, a short scope label, and a revision token. Inline descriptors are micro-labels placed adjacent to numbers when space permits; they use subdued contrast so the underlying figure remains visually dominant. The compact legend summarizes glyph meanings, group identifiers, distribution codes, and revision token formatting. Each annotated element contains metadata fields: primitive type, scope expression, qualifier, source pointer, and revision reference. Source pointers may be a short alphanumeric reference that links via an external export or a footnote area in digital display. Revision references resolve to time-stamped tokens that record author identity and a brief explanatory annotation. This arrangement prioritizes clarity and traceability: a reader can identify which markers apply to which figures and follow the lineage of any representational change without inference beyond the recorded annotations.
Grouping, distributions, and membership rules
Group definitions are explicit entries that include an identifier, a descriptive label, and a membership rule expressed in concise natural language. Membership rules state criteria for inclusion, such as column reference, tag match, or value range. Distribution entries reference an allocation set and an arithmetic descriptor: absolute allocation lists explicit quantities; proportional allocation uses ratio notation; baseline-indexed allocation references a baseline line or value. When multiple distribution schemes coexist, each is given a short code and a column marker to preserve disambiguation. Membership is resolvable by inspecting the group membership token adjacent to each line or via the legend. In dense records, grouping and distribution markers use collapsed forms with explicit expansion controls that reveal full details in a footnote or modal. The format is descriptive: it records which lines are members of each group, how a quantity is distributed among members, and which primitive records the distribution specification. This design supports interpretive clarity and archival inspection while minimizing visual interference with primary numerical data.
Data export and accessibility
Annotation layers are intended to be machine-resolvable. Export formats include a plain-text summary of the legend, a CSV with inline primitive pointers, and a structured machine-readable export where each primitive is represented as a record with fields for primitive type, scope expression, qualifier, source pointer, and revision token. Accessibility considerations require that annotations be exposed in alternate text or adjacent plain-text sections so assistive technologies can present the same semantics as the visual ledger. The notation schema includes guidance on how to render glyph meanings and group definitions in accessible formats. Exports maintain the distinction between primary figures and annotation metadata so consumers can reconstruct the interpretive layer without altering the underlying values.
Contact for record examples
To request clarification about a specific record example or to discuss notation choices for a dataset, contact the documentation team. Communications are limited to descriptive clarification about representation and do not provide advisory services.