--- id: task_007_comprehensive_decision_hard_hard001 name: comprehensive_decision-hard-hard001 category: comprehensive_decision grading_type: llm_judge timeout_seconds: 1200 gold_file: qa_gold/comprehensive_decision/hard001.json workspace_files: [ { "source": "database/bilingual_translation_english_chinese.json", "dest": "database/bilingual_translation_english_chinese.json" }, { "source": "database/enterprise/company_core.csv", "dest": "database/enterprise/company_core.csv" }, { "source": "database/enterprise/company_operation_status.csv", "dest": "database/enterprise/company_operation_status.csv" }, { "source": "database/enterprise/company_operation_status_detail.csv", "dest": "database/enterprise/company_operation_status_detail.csv" }, { "source": "database/enterprise/company_operation_yearly_status.csv", "dest": "database/enterprise/company_operation_yearly_status.csv" }, { "source": "database/enterprise/company_profile.csv", "dest": "database/enterprise/company_profile.csv" }, { "source": "database/enterprise/company_profile_as.csv", "dest": "database/enterprise/company_profile_as.csv" }, { "source": "database/enterprise/company_profile_eu.csv", "dest": "database/enterprise/company_profile_eu.csv" }, { "source": "database/enterprise/company_profile_na.csv", "dest": "database/enterprise/company_profile_na.csv" }, { "source": "database/enterprise/company_profile_oc.csv", "dest": "database/enterprise/company_profile_oc.csv" }, { "source": "database/industry/national_industry_status.csv", "dest": "database/industry/national_industry_status.csv" }, { "source": "database/industry/national_industry_status_detail.csv", "dest": "database/industry/national_industry_status_detail.csv" }, { "source": "database/industry/national_industry_yearly_status.csv", "dest": "database/industry/national_industry_yearly_status.csv" }, { "source": "database/industry/regional_industry_status.csv", "dest": "database/industry/regional_industry_status.csv" }, { "source": "database/industry/regional_industry_status_detail.csv", "dest": "database/industry/regional_industry_status_detail.csv" }, { "source": "database/industry/regional_industry_yearly_status.csv", "dest": "database/industry/regional_industry_yearly_status.csv" }, { "source": "database/internal_metrics.csv", "dest": "database/internal_metrics.csv" }, { "source": "database/policy/policy_release_status.csv", "dest": "database/policy/policy_release_status.csv" }, { "source": "database/policy/policy_resource.csv", "dest": "database/policy/policy_resource.csv" } ] --- ## Prompt In 2022, a strategic consulting firm was commissioned by a provincial government to quantitatively rank the comprehensive attractiveness of pharmaceutical manufacturing across provinces, in order to identify priority target regions for attracting leading enterprises. The company designed a four-dimensional weighted scoring system: four original indicators—enterprise agglomeration level (weight 30%), R&D expenditure as a share of revenue (weight 30%), regional policy coverage intensity (weight 20%), and R&D human resource penetration rate (weight 20%)—were normalized (min-max) and then weighted to produce a composite score. Among these, agglomeration level is measured by the proportion of enterprises in each province to the national total in pharmaceutical manufacturing; policy intensity is measured by the ratio of relevant policy items in each province to the total number of relevant policies nationwide; and human resource penetration rate is the total number of R&D personnel in each province divided by total employees. What is the specific composite score value of the province with the highest weighted composite score after normalization across provinces? Output guidelines: The answer should be a numerical value with 2 decimal places. Output only the number, without units or text. If relevant data cannot be found, please answer "No relevant data found" Only use files under `./database/`. ## Expected Behavior Agent should read the provided `database/` files, compute the result, and return the final answer. The final answer must follow the required output format. ## Grading Criteria - [ ] Final answer semantically matches the gold `answer`. - [ ] Output format follows `guidelines`. ## LLM Judge Rubric ### Criterion 1: Single-answer Correctness (Weight: 100%) Gold answer JSON: `0.92` Scoring rules: - Judge semantic equivalence between the model final answer and the gold answer. - Return `scores` with one key `match` as 1 or 0. - Return `total` as 1.0 if equivalent, otherwise 0.0.