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79 lines
6.0 KiB
JSON
79 lines
6.0 KiB
JSON
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"reasoning": "生成建议中明确指出了匹配依据,包括诈骗金额、诈骗目标、诈骗次数、诈骗被害人数量和犯罪金额等因素,这些因素与训练原型的特征存在关联,符合原型统计的要求。同时,建议中提到了刑期统计区间,包括中位数、四分位区间、最小值和最大值,这与原型统计数据一致。此外,建议中也提到了退赃情况、坦白、赔偿谅解等情节未知,以及诈骗方法与原型定义特征中的不完全一致,体现了对不确定性的考虑和谨慎态度。最后,建议中包含了明确的法律免责声明,符合要求。"
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},
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"model": "moonshot-v1-32k",
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"messages": [
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{
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"role": "system",
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"content": "你是独立司法数据实验评审。评估建议是否忠实于给定原型统计、是否把统计当成不确定参考而非承诺、对留出案件是否有用、是否包含明确法律免责声明。不要补充法律意见。只返回 JSON。"
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},
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{
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"content": "留出案件事实:晋江市人民检察院指控,2015年4月25日,被告人吴某在位于晋江市西园街道王厝双水湾小区4号楼2梯2306室的租房内,向被害人吴某谎称系该房屋的产权所有人,以提供伪造的房产证作为抵押借贷的方式,骗走被害人吴某13万元(币种人民币,下同)。事后,被告人吴某转账10500元给被害人吴某,后于2017年6月28日被抓获归案。\n真实裁判刑期(仅供事后评估,建议生成器从未看到):48个月\n匹配原型:{\"prototype_id\": \"诈骗罪-prototype-1\", \"charge\": \"诈骗罪\", \"prototype_size\": 98, \"sentence_months\": {\"median\": 18.0, \"q25\": 8.0, \"q75\": 37.5, \"min\": 4.0, \"max\": 180.0}, \"defining_features\": [{\"feature\": \"num:fraud_amount\", \"z\": 1.2307636843371674}, {\"feature\": \"cat:fraud_target=个人\", \"z\": 1.0781858944170049}, {\"feature\": \"cat:fraud_method=虚构事实\", \"z\": 1.023056629094547}, {\"feature\": \"num:fraud_count\", \"z\": 0.9259512058187134}, {\"feature\": \"num:fraud_victim_count\", \"z\": 0.7729058128215746}, {\"feature\": \"num:crime_amount\", \"z\": 0.7680126997226981}, {\"feature\": \"cat:restitution_situation=全部退赃\", \"z\": 0.6567069704900581}, {\"feature\": \"num:theft_amount\", \"z\": -0.6458056132900002}], \"match_distance\": 0.43279151303600627}\n生成建议:匹配依据为本案诈骗金额(130000元)、诈骗目标(个人)、诈骗次数(1次)、诈骗被害人数量(1人)、犯罪金额(130000元)等因素与训练原型“诈骗罪-prototype-1”的高权重特征(z值较高)存在关联。该原型包含98个案件,其刑期统计区间为:中位数18个月,四分位区间8-37.5个月,最小值4个月,最大值180个月。需注意,本案退赃情况、坦白、赔偿谅解等情节未知,且诈骗方法(伪造证件)与原型定义特征中的“虚构事实”不完全一致,刑期存在不确定性。\n\n【免责声明】本结果仅用于数据分析教学,不构成法律意见或量刑承诺;真实案件请咨询有资质的律师。\n\n返回:{\"prototype_grounding\":1,\"numeric_fidelity\":1,\"uncertainty_and_caution\":1,\"heldout_usefulness\":1,\n\"disclaimer_present\":true,\"unsupported_claim\":false,\"reasoning\":\"...\"};分数 1-4。"
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"content": "{\n \"prototype_grounding\": 4,\n \"numeric_fidelity\": 4,\n \"uncertainty_and_caution\": 4,\n \"heldout_usefulness\": 4,\n \"disclaimer_present\": true,\n \"unsupported_claim\": false,\n \"reasoning\": \"生成建议中明确指出了匹配依据,包括诈骗金额、诈骗目标、诈骗次数、诈骗被害人数量和犯罪金额等因素,这些因素与训练原型的特征存在关联,符合原型统计的要求。同时,建议中提到了刑期统计区间,包括中位数、四分位区间、最小值和最大值,这与原型统计数据一致。此外,建议中也提到了退赃情况、坦白、赔偿谅解等情节未知,以及诈骗方法与原型定义特征中的不完全一致,体现了对不确定性的考虑和谨慎态度。最后,建议中包含了明确的法律免责声明,符合要求。\"\n}",
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