The AI Search Era:A Brand-New Blueprint for Brands from GEO to AIBE (GEO White Paper 2026)
Bibliographic record
Abstract
从“前沿理论”到“营销基石”，GEO白皮书将为你解答：<br/>AI应用趋势洞察：解析AI应用及AI搜索在全球范围的演进趋势;<br/>GEO的定义与核心逻辑：系统解读GEO的核心概念以及与SEO的根本性差异;<br/>GEO对品牌的价值和必要性：为什么现在是布局生成式引擎生态的窗口期;<br/>GEO的监测和优化支持体系：全面介绍GEO执行前必须要经历的五大关键步骤;<br/>GEO的实操策略与落地方法：GEO策略的核心执行要素有哪些。
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.014 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".