AIGC Report Interpretation Guide | Read Scores, Segment Labels, Heatmaps, and Platform Fields

Direct answer for this topic

Learn how to interpret an AIGC detection report before editing: total score fields, segment labels, heatmap-style marks, section distribution, and platform-specific terminology.

• Read the dashboard fields before deciding what to edit
• Separate total score, segment labels, heatmap marks, and section distribution
• Useful when comparing CNKI, Turnitin, and other report layouts
• This page is for reading the report itself: what the score field means, what passage labels indicate, and how the marked sections are distributed across the document.

Why this page is suitable for citation

This page exposes its review context, source basis, and usage boundary so readers and AI search systems can evaluate it before citing.

Related workflows and reference pages

What this page helps you do first

• Read the dashboard fields before deciding what to edit
• Separate total score, segment labels, heatmap marks, and section distribution
• Useful when comparing CNKI, Turnitin, and other report layouts

What this page does before revision starts

This page is for reading the report itself: what the score field means, what passage labels indicate, and how the marked sections are distributed across the document.

Do not treat it as a rewrite recipe. The first job is to understand the evidence shown by the report so later revision decisions are based on the right field, not a single headline number.

Read the report fields in four layers

• Score panel: total rate, suspected proportion, and any sub-category numbers
• Segment labels: sentence, paragraph, or block-level marks and their color meaning
• Distribution view: whether marks appear as isolated points, clusters, or section bands
• Export details: original paragraph order, evidence snippets, and platform notes

How to compare platform terminology

• One report may call a mark AI-like, another may call it machine-generated or suspicious
• Color depth usually signals confidence or severity, but the legend must be checked first
• School-designated reports should be read with their own labels, not translated mechanically from another platform
• Screenshots are less useful than exportable paragraph-level fields when you need a later action plan

A safer interpretation order

• Read the legend and score definitions first
• Map marked blocks to thesis sections such as abstract, introduction, method, or conclusion
• Record whether each mark is isolated, repeated, or part of a long band
• Only after interpretation should you decide whether a CNKI-specific or general reduction workflow is needed

Start from the matrix page if this issue is part of a larger workflow

If this problem is only one step inside a bigger submission, citation, detection, or outline workflow, start from the matrix page below and then return to this specialist guide.

Common university scenarios for this issue

If you are solving this problem under a specific university format, check the relevant school requirement pages below before making final edits.

Frequently asked questions

Does a low total rate always mean the paper is safe?

Not always. Concentrated risk in key sections can still require action even when the overall rate looks moderate.

If different platforms disagree, does that mean the report is unreliable?

Not necessarily. Platform differences are normal. Focus on the school-designated platform and on passages repeatedly highlighted across systems.

Should I rewrite the whole paper first or start with flagged passages?

Start with the concentrated flagged passages, especially in sensitive sections. That is usually the highest-yield move.