liqiang
b119135836
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90 lines
8.5 KiB
JSON
90 lines
8.5 KiB
JSON
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"advice": "匹配依据：待匹配案件与训练集原型“盗窃罪-prototype-1”共享“theft_location=公共场所”“theft_method=趁人不备”两个关键特征，这两个特征在原型定义特征中具有较高z值（分别为3.096、2.789），表明与原型关联性较强。统计区间：该原型样本量为1，刑期中位数、四分位距（q25、q75）、最小值及最大值均为4.0个月。不确定性：待匹配案件存在“joint_crime=true”“crime_count=2.0”“crime_amount=11304.0”等原型未包含的因素，且原型关键特征如“theft_tool=筷子”“disposal_of_stolen_property=全部发还”等在待匹配案件中缺失或未提及，同时原型样本量较小，可能导致刑期参考存在偏差。"
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"content": "{\"advice\":\"匹配依据：待匹配案件与训练集原型“盗窃罪-prototype-1”共享“theft_location=公共场所”“theft_method=趁人不备”两个关键特征，这两个特征在原型定义特征中具有较高z值（分别为3.096、2.789），表明与原型关联性较强。统计区间：该原型样本量为1，刑期中位数、四分位距（q25、q75）、最小值及最大值均为4.0个月。不确定性：待匹配案件存在“joint_crime=true”“crime_count=2.0”“crime_amount=11304.0”等原型未包含的因素，且原型关键特征如“theft_tool=筷子”“disposal_of_stolen_property=全部发还”等在待匹配案件中缺失或未提及，同时原型样本量较小，可能导致刑期参考存在偏差。\"}",
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"reasoning_content": "\n我现在需要处理用户提供的司法数据分析请求。首先，我要仔细阅读用户给出的待匹配案件的抽取因素（HELDOUT EXTRACTED FACTORS）和匹配到的训练集原型（MATCHED TRAINING PROTOTYPE ONLY）。\n\n首先，待匹配案件的罪名是“盗窃罪”，关键因素包括：theft_location=公共场所，theft_method=趁人不备，joint_crime=true，crime_count=2.0，crime_amount=11304.0，household_theft=false，destructive_means=false，use_special_convenience=无，其他如surrender、truthful_confession等均为null。\n\n然后看匹配到的原型，prototype_id是“盗窃罪-prototype-1”，原型大小为1，刑期统计是median=4.0，q25=4.0，q75=4.0，min=4.0，max=4.0，也就是只有一个案件，刑期固定为4个月。定义特征中，有几个与待匹配案件可能相关的：theft_location=公共场所（z值3.096），theft_method=趁人不备（z值2.789），这两个特征在待匹配案件中也存在（theft_location=公共场所，theft_method=趁人不备），所以这两个特征是匹配的。但原型的其他定义特征，如theft_tool=筷子、disposal_of_stolen_property=全部发还、stolen_property_disposal=返还被害人、theft_target=现金、stolen_property_recovery=全部追回、return_of_stolen_property=全部退赃等，在待匹配案件中要么是null（如theft_tool、disposal_of_stolen_property、stolen_property_disposal、stolen_property_recovery、return_of_stolen_property等），要么没有提到（theft_target）。\n\n接下来，根据用户要求，只能使用给出的训练集案件原型统计与已抽取因素，不能使用外部知识或原始案件。需要解释匹配依据和统计区间，强调不确定性。匹配依据主要是待匹配案件与原型共享的两个特征：theft_location=公共场所和theft_method=趁人不备，这两个特征在原型的定义特征中z值较高，说明是原型的重要特征。但原型的其他关键特征（如theft_tool=筷子、退赃、发还等）在待匹配案件中缺失或未提及，可能影响匹配的准确性。统计区间方面，原型的刑期中位数、四分位距、 min和max均为4.0，但原型大小仅为1，样本量小，统计结果的可靠性可能较低。不确定性体现在待匹配案件存在joint_crime=true等原型未包含的因素，且缺失原型的部分关键特征，可能导致刑期预测存在偏差。\n\n最后，按照要求返回JSON，格式为{\"advice\":\"...\"}，内容包括匹配依据、统计区间、不确定性说明，不使用外部知识，不给出其他刑期数字，只基于原型的统计。"
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