{ "signature": "b2a585ba80d75b1dccd6678db8f101a42858aeae5e72a2d35d43852b73f89558", "parsed": { "advice": "匹配依据:待预测案件的“proceeds_use=挥霍”“booty_squandered=true”“disposal_of_stolen_property=挥霍”“fraud_target=个人”等特征与匹配原型“诈骗罪-prototype-0”的关键定义特征(z值较高)相符。该原型包含22个案件,刑期统计区间为四分位区间11.25个月至58.5个月,中位数为45.0个月。需注意,案件中“restitution_situation”“return_of_stolen_property”等特征为null,与原型中部分定义特征(如“restitution_situation=部分退赃”)存在差异,可能影响刑期预测,故最终刑期存在不确定性,可能落在上述统计区间内。" }, "receipt": { "purpose": "3-12 held-out prototype-grounded advice cail2018-2e21e0717140020b", "provider": "ark", "endpoint": "https://ark.cn-beijing.volces.com/api/v3", "started_at": "2026-07-30T04:42:35.925809+00:00", "latency_ms": 18766.357, "request": { "model": "doubao-seed-1-6-250615", "messages": [ { "role": "system", "content": "你是司法数据分析助手。只可使用给出的训练集案件原型统计与已抽取因素,不得使用原始训练案件、外部法律知识或自行给出其他刑期数字。解释匹配依据和统计区间,强调不确定性。只返回 JSON:{\"advice\":\"...\"}。不要写免责声明,系统会统一附加。" }, { "role": "user", "content": "HELDOUT EXTRACTED FACTORS:\n{\"charge\": \"诈骗罪\", \"surrender\": null, \"truthful_confession\": null, \"compensation_to_victim\": null, \"victim_forgiveness\": null, \"joint_crime\": null, \"criminal_record\": true, \"arrest_method\": null, \"return_of_stolen_property\": null, \"crime_count\": 1.0, \"crime_amount\": 20000.0, \"guilty_plea\": null, \"first_offense\": null, \"recidivism\": true, \"criminal_form\": null, \"disposal_of_stolen_property\": \"挥霍\", \"victim_count\": 1.0, \"fraud_count\": 1.0, \"fraud_amount\": 20000.0, \"fraud_method\": \"虚构事实\", \"restitution_situation\": null, \"fraud_target\": \"个人\", \"proceeds_use\": \"挥霍\", \"impersonate_identity\": null, \"fraud_means\": \"虚假贷款\", \"fraud_victim_count\": 1.0, \"booty_squandered\": true, \"fraud_middleman\": null}\n\nMATCHED TRAINING PROTOTYPE ONLY:\n{\"prototype_id\": \"诈骗罪-prototype-0\", \"charge\": \"诈骗罪\", \"prototype_size\": 22, \"sentence_months\": {\"median\": 45.0, \"q25\": 11.25, \"q75\": 58.5, \"min\": 3.0, \"max\": 168.0}, \"defining_features\": [{\"feature\": \"cat:proceeds_use=挥霍\", \"z\": 3.726229148285477}, {\"feature\": \"bool:booty_squandered\", \"z\": 3.629934841284343}, {\"feature\": \"cat:disposal_of_stolen_property=挥霍\", \"z\": 2.823242590137243}, {\"feature\": \"cat:restitution_situation=部分退赃\", \"z\": 2.149983260084719}, {\"feature\": \"num:fraud_victim_count\", \"z\": 1.7078406967199085}, {\"feature\": \"cat:fraud_target=个人\", \"z\": 1.6821971448205932}, {\"feature\": \"num:fraud_amount\", \"z\": 1.6088028147438735}, {\"feature\": \"cat:return_of_stolen_property=部分退赃\", \"z\": 1.5149027385089702}], \"match_distance\": 0.16728335313876955}" } ], "temperature": 0, "seed": 37, "max_tokens": 900, "response_format": { "type": "json_object" } }, "response": { "id": "021785386556164c85c17782ebc45169ced08d4429f63da29d0fa", "choices": [ { "finish_reason": "stop", "index": 0, "logprobs": null, "message": { "content": "{\"advice\":\"匹配依据:待预测案件的“proceeds_use=挥霍”“booty_squandered=true”“disposal_of_stolen_property=挥霍”“fraud_target=个人”等特征与匹配原型“诈骗罪-prototype-0”的关键定义特征(z值较高)相符。该原型包含22个案件,刑期统计区间为四分位区间11.25个月至58.5个月,中位数为45.0个月。需注意,案件中“restitution_situation”“return_of_stolen_property”等特征为null,与原型中部分定义特征(如“restitution_situation=部分退赃”)存在差异,可能影响刑期预测,故最终刑期存在不确定性,可能落在上述统计区间内。\"}", "refusal": null, "role": "assistant", "annotations": null, "audio": null, "function_call": null, "tool_calls": null, "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,我要仔细查看用户给出的待预测案件的抽取因素(HELDOUT EXTRACTED FACTORS)和匹配的训练集原型(MATCHED TRAINING PROTOTYPE ONLY)。\n\n首先,待预测案件的罪名是诈骗罪,关键因素包括:proceeds_use=挥霍,booty_squandered=true,disposal_of_stolen_property=挥霍,fraud_target=个人,fraud_amount=20000.0,fraud_victim_count=1.0,同时有criminal_record=true和recidivism=true,但这些在匹配的原型中可能不是定义特征。\n\n匹配的原型是“诈骗罪-prototype-0”,原型大小为22个案件,刑期统计包括中位数45.0个月,Q25=11.25,Q75=58.5,min=3.0,max=168.0。定义特征中,前三个都是与“挥霍”相关的:proceeds_use=挥霍、booty_squandered(布尔值)、disposal_of_stolen_property=挥霍,这些待预测案件都匹配(proceeds_use=挥霍,booty_squandered=true,disposal_of_stolen_property=挥霍)。另外,fraud_target=个人也在定义特征中,待预测案件也是个人。fraud_amount和fraud_victim_count是数值型定义特征,待预测案件有这些数值(20000.0和1.0)。不过,原型的定义特征中还有“restitution_situation=部分退赃”和“return_of_stolen_property=部分退赃”,但待预测案件中restitution_situation和return_of_stolen_property都是null,这可能存在不匹配,但匹配距离是0.167,相对较低,说明整体匹配度尚可。\n\n接下来,根据要求,只能使用训练集原型的统计数据,不能用外部知识或原始案件。需要解释匹配依据、统计区间,并强调不确定性。匹配依据主要是待预测案件的proceeds_use=挥霍、booty_squandered=true、disposal_of_stolen_property=挥霍、fraud_target=个人等特征与原型的定义特征匹配。统计区间应使用原型的四分位区间(Q25=11.25,Q75=58.5)和中位数45.0。需要说明刑期可能在该区间内,但存在不确定性,因为案件可能存在未匹配的特征(如restitution_situation为null,而原型中有部分退赃的特征)。\n\n最后,按照格式要求返回JSON,键为\"advice\",值为上述解释内容,注意不要使用Markdown,保持自然语言,强调不确定性,不给出具体刑期数字,只提统计区间。" 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