Reading / Continuous reading

Research paper

Typeset an abstract, an argument, equations and references with the report reading scale.

Two columns with a full-width figure

Preview

Uncertainty in a binary source

Abstract

Entropy and normalized variance share their endpoints and maximum, but differ between them.

Definition

Binary entropy measures uncertainty in bits. It is zero at either deterministic endpoint and reaches one bit when the two outcomes are equally probable.

Normalized variance also reaches one at equal probabilities. Its shared numerical range helps compare the shapes, but does not give variance the unit “bit”.

Values calculated at equal probabilities.

Probability p	Entropy H [bit]	Variance V [1]
0.5	1	1

Interpretation

A common maximum does not make the two functions interchangeable. Definitions and units remain essential to interpreting the figure.

These values describe known theoretical functions. They do not estimate uncertainty from a measured sample or demonstrate compression performance.

HTML

<article class="sheet sheet-paper" lang="en" data-dd-color="grayscale" data-dd-paper="a4">
  <header class="paper-head"><h2>Uncertainty in a binary source</h2></header>
  <section class="abstract prose">
    <h3>Abstract</h3>
    <p>Entropy and normalized variance share their endpoints and maximum, but differ between them.</p>
  </section>
  <div class="paper-columns">
    <section class="prose">
      <h3>Definition</h3>
      <p>Binary entropy measures uncertainty in bits. It is zero at either deterministic endpoint and reaches one bit when the two outcomes are equally probable.</p>
      <p>Normalized variance also reaches one at equal probabilities. Its shared numerical range helps compare the shapes, but does not give variance the unit “bit”.</p>
    </section>
  </div>
  <figure class="plate plate-table plate-full">
    <figcaption>Values calculated at equal probabilities.</figcaption>
    <div class="table-wrap"><table>
      <thead><tr><th>Probability p</th><th>Entropy H [bit]</th><th>Variance V [1]</th></tr></thead>
      <tbody><tr><td>0.5</td><td>1</td><td>1</td></tr></tbody>
    </table></div>
  </figure>
  <div class="paper-columns">
    <section class="prose">
      <h3>Interpretation</h3>
      <p>A common maximum does not make the two functions interchangeable. Definitions and units remain essential to interpreting the figure.</p>
      <p>These values describe known theoretical functions. They do not estimate uncertainty from a measured sample or demonstrate compression performance.</p>
    </section>
  </div>
</article>

A paper with an abstract

Preview

Uncertainty in a binary source

An expository typesetting specimen

Abstract

Entropy and normalized variance share their endpoints and maximum, but differ between them.

Definition

Both curves are calculated directly from their definitions.

HTML

<article class="sheet sheet-paper" lang="en" data-dd-color="grayscale" data-dd-paper="a4" data-dd-print-urls="sources">
  <header class="paper-head">
    <h2>Uncertainty in a binary source</h2>
    <p class="paper-byline">An expository typesetting specimen</p>
  </header>
  <section class="abstract prose">
    <h3>Abstract</h3>
    <p>Entropy and normalized variance share their endpoints and maximum, but differ between them.</p>
  </section>
  <section class="prose">
    <h3>Definition</h3>
    <p>Both curves are calculated directly from their definitions.</p>
  </section>
</article>

A numbered equation

Preview

V (p) = 4 p (1 - p)

(1)

HTML

<div class="equation" id="binary-variance">
  <div class="eq-body" tabindex="0" role="region" aria-label="Normalized variance">
    <math display="block" translate="no">
      <mi>V</mi><mo>(</mo><mi>p</mi><mo>)</mo><mo>=</mo>
      <mn>4</mn><mi>p</mi><mo>(</mo><mn>1</mn><mo>−</mo><mi>p</mi><mo>)</mo>
    </math>
  </div>
  <a class="eq-number" href="#binary-variance" aria-label="Equation 1">(1)</a>
</div>

Classes & attributes

Class / attribute	Purpose
`.sheet.sheet-paper`	A continuous single-column paper using the existing reading scale and shared prose and figure widths.
`.paper-columns`	Wrap prose sections in a two-column reading flow. Columns use the existing type size and become one column when the containing width is too narrow.
`.plate-full between .paper-columns blocks`	Place a full-width figure or table between column flows. Text resumes below it, including in print. Keep column-width figures and tables inside the flow.
`.paper-head / .paper-byline / .abstract`	Title, authors or specimen attribution, and abstract. Use ordinary headings and prose for sections.
`.equation / .eq-body / .eq-number`	A native MathML display, its keyboard-scrollable wrapper, and an optional author-supplied number. Use .ref for references.
`data-dd-color="grayscale" / "monochrome"`	Neutral inks or black and white, on the root or a document wrapper. The light/dark theme still applies; print uses light. Omit for colour.
`--dd-font-math`	Optional mathematical font family. Defaults to the browser’s math font; no font download is required.

Usage notes

The research-paper example calculates its graph and table from stated functions. Mark series with dashes and shapes as well as colour. Colour modes cover doc-ui tokens, not raster images or custom colours. Split long equations into MathML table rows to fit print; horizontal scrolling is only available on screen. Authors own equation numbers and their references. Keep titles and abstracts outside .paper-columns, and full-width plates between flows. Write content in reading order without manual left and right blocks. This is a general composition, not a publisher-specific template.

Examples follow your system color scheme. Use ◐ in the header to try automatic, light, and dark themes.

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