{
 "title": "AI content quality: 29 AI-assisted drafts scored before publishing",
 "source": "ForgeRank AI external scoring runs (external_scores rows), September 2026",
 "method": {
  "dimensions": [
   "real information gain",
   "specificity density",
   "source compliance",
   "argument depth"
  ],
  "aggregation": "median of 3 samples per dimension; headline score = average of the four dimensions",
  "note": "dimensions are judged in tiers (4/7/8/9 observed across this set), which is why the headline score moves in steps"
 },
 "count": 29,
 "distribution": {
  "8.5": 1,
  "7.8": 1,
  "7.2": 4,
  "7.0": 3,
  "4.8": 10,
  "4.0": 10
 },
 "below_5_0": 20,
 "between_5_0_and_6_9": 0,
 "capped_source_analysis": 13,
 "coverage_min_pct": 57.8,
 "coverage_max_pct": 93.5,
 "rows": [
  {
   "platform": "google",
   "chars": 11954,
   "score": 8.5,
   "dimensions": [
    {
     "dimension": "真实信息增益",
     "score": 8.0
    },
    {
     "dimension": "具体性密度",
     "score": 9.0
    },
    {
     "dimension": "素材来源合规",
     "score": 9.0
    },
    {
     "dimension": "论证/机制深度",
     "score": 8.0
    }
   ],
   "quality_level": "high",
   "industry": "Technology & Computing",
   "pass_line": 8.5,
   "pro_score": 6.0,
   "ai_tone": {
    "verdict": "AI-HEAVY",
    "families": [
     {
      "family": "ban_connect_words",
      "hits": 6
     },
     {
      "family": "transition",
      "hits": 6
     },
     {
      "family": "semicolon",
      "hits": 3
     },
     {
      "family": "ban_patterns",
      "hits": 3
     },
     {
      "family": "hedge",
      "hits": 2
     },
     {
      "family": "helper_tone",
      "hits": 1
     },
     {
      "family": "vague_claim",
      "hits": 1
     },
     {
      "family": "meta_label_leak",
      "hits": 1
     },
     {
      "family": "youll_benefit_tell",
      "hits": 1
     },
     {
      "family": "em_dash",
      "hits": 1
     },
     {
      "family": "negation_contrast",
      "hits": 1
     },
     {
      "family": "tutorial_label",
      "hits": 1
     },
     {
      "family": "not_xy",
      "hits": 1
     }
    ]
   },
   "source_analysis": {
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    "claims_analyzed": 100,
    "required": 0,
    "required_missing_source": 0,
    "status": "ok_capped"
   },
   "scored_at": "2026-09-11T10:11:07.653406+00:00"
  },
  {
   "platform": "google",
   "chars": 5641,
   "score": 7.0,
   "dimensions": [
    {
     "dimension": "真实信息增益",
     "score": 7.0
    },
    {
     "dimension": "具体性密度",
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    },
    {
     "dimension": "素材来源合规",
     "score": 7.0
    },
    {
     "dimension": "论证/机制深度",
     "score": 7.0
    }
   ],
   "quality_level": "medium",
   "industry": "Technology & Computing",
   "pass_line": 8.5,
   "pro_score": null,
   "ai_tone": {
    "verdict": "AI-HEAVY",
    "families": [
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      "family": "negation_pivot",
      "hits": 1
     },
     {
      "family": "negation_pivot2",
      "hits": 1
     },
     {
      "family": "meta_label_leak",
      "hits": 1
     },
     {
      "family": "semicolon",
      "hits": 1
     },
     {
      "family": "ban_patterns",
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     },
     {
      "family": "negation_contrast",
      "hits": 1
     },
     {
      "family": "dual_contrast",
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     }
    ]
   },
   "source_analysis": {
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    "claims_analyzed": 47,
    "required": 0,
    "required_missing_source": 0,
    "status": "ok"
   },
   "scored_at": "2026-09-11T10:13:27.346281+00:00"
  },
  {
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   "chars": 12006,
   "score": 4.8,
   "dimensions": [
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    },
    {
     "dimension": "具体性密度",
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    },
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    },
    {
     "dimension": "论证/机制深度",
     "score": 4.0
    }
   ],
   "quality_level": "low",
   "industry": "Technology & Computing",
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   "pro_score": null,
   "ai_tone": {
    "verdict": "AI-TINGED",
    "families": [
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      "hits": 4
     },
     {
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      "hits": 1
     },
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     },
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     },
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     }
    ]
   },
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    "claims_analyzed": 62,
    "required": 0,
    "required_missing_source": 0,
    "status": "ok"
   },
   "scored_at": "2026-09-11T10:18:26.609288+00:00"
  },
  {
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   "score": 4.0,
   "dimensions": [
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    },
    {
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    },
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    }
   ],
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     },
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     },
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     },
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     },
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     },
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     },
     {
      "family": "meta_label_leak",
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     }
    ]
   },
   "source_analysis": {
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    "claims_analyzed": 100,
    "required": 4,
    "required_missing_source": 2,
    "status": "ok_capped"
   },
   "scored_at": "2026-09-11T10:19:41.037211+00:00"
  },
  {
   "platform": "google",
   "chars": 19187,
   "score": 7.8,
   "dimensions": [
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     "dimension": "具体性密度",
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    },
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    },
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    }
   ],
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   "pro_score": 7.0,
   "ai_tone": {
    "verdict": "AI-HEAVY",
    "families": [
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      "hits": 7
     },
     {
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     },
     {
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     },
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   },
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    "claims_analyzed": 100,
    "required": 8,
    "required_missing_source": 4,
    "status": "ok_capped"
   },
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   "scored_at": "2026-09-11T11:14:43.509934+00:00"
  }
 ],
 "version": "1.0.0",
 "citation": "ForgeRank AI. (2026). AI Content Quality: 29 AI-Assisted Drafts Scored Before Publishing, v1.0.0. https://forgerankai.com/static/data/ai-content-quality-29-drafts.json",
 "citation_html": "ForgeRank AI. (2026). <i>AI Content Quality: 29 AI-Assisted Drafts Scored Before Publishing</i>, v1.0.0. <a href=\"https://forgerankai.com/static/data/ai-content-quality-29-drafts.json\">https://forgerankai.com/static/data/ai-content-quality-29-drafts.json</a>",
 "publisher": {
  "name": "ForgeRank AI",
  "url": "https://forgerankai.com",
  "about": "https://forgerankai.com/about"
 },
 "usage": "可自由引用与二次使用，请附上 citation 字段给出的出处。本文件只含**派生测量与元数据**（每篇的字数、四维分、措辞家族命中、来源核查计数等），**不含任何草稿正文**，因此不涉及被评文本的再分发。数据集来自我们自己的评分运行，不是第三方数据转售。",
 "schema": {
  "rows[].platform": "评分所用平台口径（google / 其他），影响评分器读取方式",
  "rows[].chars": "草稿字符数",
  "rows[].score": "四维平均后的头条分（0–10）",
  "rows[].dimensions": "四个维度各自的分：real information gain / specificity density / source compliance / argument depth",
  "rows[].quality_level": "评分器给出的档位标签",
  "rows[].industry": "行业分类（影响评分器的领域先验）",
  "rows[].pass_line": "该次运行使用的通过线",
  "rows[].pro_score": "单独的 PRO 口径得分（与头条分不是同一个量）",
  "rows[].ai_tone": "AI 腔 lint 的判定与逐句式族命中数；verdict 与 families 两字段",
  "rows[].source_analysis": "来源核查计数（claims_total / required / missing 等）",
  "rows[].scored_at": "评分时间戳",
  "distribution": "头条分分布（分档 → 篇数）",
  "capped_source_analysis": "来源核查被上限截断的篇数",
  "coverage_min_pct / coverage_max_pct": "目标关键词覆盖率的最小/最大值"
 },
 "how_to_reproduce": "评分口径与四维定义见 https://forgerankai.com/blog/ai-content-quality-data；同一文本跨次运行可差 ±0.3–0.5，因此单篇分数应视为区间而非定点值。",
 "known_limitations": [
  "同一段文本两次评分可以相差 ±0.3–0.5 分；单次得分不能当作可复现的定点值。",
  "本集全部为 AI 辅助草稿，**没有人类对照组**，因此不能用于估计「人类写作被误判」的比例。",
  "维度的分档形态（4/7/8/9）是评分器设计的结果，不是自然连续的测量。"
 ]
}