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Use ONLY the original visible task contract below. No network, no hidden tests, no reference solution.\n\nTransport mapping: return final deliverable as exactly one JSON object:\n{\n \"report\": {\n \"per_query\": {\n \"\": {\n \"precision_at_k\": ,\n \"recall_at_k\": ,\n \"mrr\": ,\n \"retrieved\": [\"\", ...]\n }\n },\n \"macro_avg\": {\n \"precision_at_k\": ,\n \"recall_at_k\": ,\n \"mrr\": \n },\n \"K\": 5\n }\n}\n\n===== ORIGINAL TASK CONTRACT =====\n# Retrieval Evaluation Harness\n\nBuild a retrieval evaluation harness that:\n1. Defines a small text corpus (at least 10 documents) with stable string doc_id values.\n2. Defines at least 5 queries, each with a set of relevant doc_ids (qrels).\n3. Implements a sparse retriever over the corpus.\n4. For each query, retrieves exactly K = 5 results as (doc_id, score, rank) tuples.\n5. Computes per-query Precision@5, Recall@5, and MRR.\n6. Macro-averages each metric across all queries.\n7. Writes the evaluation report to report.json.\n\nNo external input files. The corpus, queries, and qrels are defined inside the solution.\n\nOutput report.json:\n{\n \"per_query\": {\n \"\": {\n \"precision_at_k\": ,\n \"recall_at_k\": ,\n \"mrr\": ,\n \"retrieved\": [\"\", ...]\n }\n },\n \"macro_avg\": {\n \"precision_at_k\": ,\n \"recall_at_k\": ,\n \"mrr\": \n },\n \"K\": 5\n}\n\nConstraints:\n- Each query returns exactly K=5 results; validate this.\n- Metrics are computed per query using only qrels for that query.\n- Precision@K = relevant docs in top-K / K.\n- Recall@K = relevant docs in top-K / total relevant docs for that query.\n- MRR = 1/rank_of_first_relevant_doc, or 0.0 if none in top-K.\n- Macro-average = arithmetic mean of per-query metrics.\n- Tie-breaking deterministic, e.g. doc_id lexicographic.\n- Corpus has at least 10 documents and at least 5 queries with qrels.\n- All metric values are floats rounded to 6 decimal places.\n`;\nconst metric={type:\"number\"};\nconst pq={type:\"object\",additionalProperties:false,required:[\"precision_at_k\",\"recall_at_k\",\"mrr\",\"retrieved\"],properties:{precision_at_k:metric,recall_at_k:metric,mrr:metric,retrieved:{type:\"array\",minItems:5,maxItems:5,items:{type:\"string\"}}}};\nconst report={type:\"object\",additionalProperties:false,required:[\"per_query\",\"macro_avg\",\"K\"],properties:{per_query:{type:\"object\",minProperties:5,additionalProperties:pq},macro_avg:{type:\"object\",additionalProperties:false,required:[\"precision_at_k\",\"recall_at_k\",\"mrr\"],properties:{precision_at_k:metric,recall_at_k:metric,mrr:metric}},K:{type:\"integer\",enum:[5]}}};\nconst schema={type:\"object\",additionalProperties:false,required:[\"report\"],properties:{report}};\nawait blob.set(\"gdi/c8/t2/exact/user_prompt.txt\",prompt);\nawait blob.set(\"gdi/c8/t2/FINAL_SCHEMA.json\",JSON.stringify(schema,null,2));\nconst freeze={experiment_id:\"GDI_C8_T2_RETRIEVAL_EVAL_REPLICATION_2026-09-05\",corpus_id:\"GDI_SCREENING_CORPUS_C8_EXTERNAL_STATE_REPLICATION_2026-09-05\",order:2,status:\"PROSPECTIVELY_FROZEN_UNCONSUMED\",task:\"retrieval-evaluation-harness\",design_sha256:\"e0a792c362d35ec3170d23887f5bba6185271a6513b5428131e88e50849d0ce9\",prompt_sha256:await sha(prompt),prompt_bytes:new TextEncoder().encode(prompt).length,final_schema_sha256:await sha(JSON.stringify(schema)),known_benchmark_underspecification:\"Visible task allows arbitrary corpus/query IDs/qrels, while official verifier assumes specific q1..q5 identities and relevance sets. 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