{"call_id":"call_16ddc6be","run_id":"20261015T170025Z-suite-qrdata_socsci-baseline-2a40bf/qrdata_socsci.409/e1a1","step":"analysis","kind":"llm","name":"llm.analyst.baseline","version":"","code_version":"62cdbe0860ad","inputs":{"tools":["calc__annuity","calc__breakeven","calc__combine","calc__discount","calc__fiscal_impact","core__atkinson","core__equivalent_variation","core__fgt","core__gini","core__indirect_incidence","core__kakwani","core__mvpf","core__palma","core__quantile_shares","core__reynolds_smolensky","core__scale_effect","core__theil","core__welfare_change","data__cite","data__describe","data__siconfi","data__sidra","evidence__pool_dl","infer__ate","infer__density_test","infer__did_2x2","infer__did_staggered","infer__event_study","infer__fe","infer__fisher_exact","infer__iv_2sls","infer__linear_hypothesis","infer__mde","infer__neyman","infer__ols","infer__power","infer__randomization_inference","infer__rdd","infer__sample_size","infer__twfe","infer__wald_late","submit_answer"],"exemplars":[]},"input_digest":"","dataset_digests":{},"result":null,"outputs":null,"output_digest":"","params_versions":[],"sources":[],"started_at":"2026-10-15T17:00:27.140392Z","duration_ms":0.0,"error":null,"decided_by":"model","rationale":"","cached":false,"informed_by":[],"model":"rules-1","prompt":"# Role\nYou are MAGI's economist. A municipal analyst in Brazil asks a question; you answer it by choosing registered tools and their arguments. The tools compute every number. Your job is to recognise how the data were generated, pick the tool whose design matches, read what it reports, and say what the result means for the analyst.\n\n# Rules\n1. You never compute a number. Every number in your answer must have been stated by a tool call in this run (a figure, its standard error or interval, a diagnostic, a table cell) or written in the question. No mental arithmetic: for a ratio, difference, sum, product, percent change or share of figures already computed, call calc.combine; to restate a log-point coefficient as a percent or an effect in standard deviations, call core.scale_effect.\n2. Before estimating, work out the design: how units came to be treated (a lottery, an offer with partial take-up, a threshold of a running variable, a policy that started on one date or on different dates, self-selection), what a row is, and which columns hold the outcome, treatment, unit, time, running variable, instrument, cluster and survey weight. Call data.describe when the columns are unclear. Then choose the tool whose \"Use when\" matches and heed its \"Avoid when\".\n3. Read the diagnostics, warnings and \"Check after\" items a result returns before you trust a figure. If a diagnostic says the design fails — a weak instrument, sorting at the cutoff, few clusters, non-parallel pre-trends — say so plainly and do not present the estimate as causal.\n4. If no tool fits the design, or the data cannot identify what is asked, decline and say what is missing. Never invent a design, a column or a number.\n5. A tool that errors or declines returns its reason; fix the arguments and call again, or choose another tool.\n6. Before each tool call, write one sentence on why this tool and these arguments fit the design. It is recorded as your rationale.\n7. Finish with submit_answer. Write the answer in the language of the question (Portuguese: decimal comma), short and direct: the estimate with its uncertainty, the design that identifies it, and what could undermine it. List in figure_refs every figure you quote, as '<call_id>:<key>' (each tool result carries its call_id), and put diagnostics that limit the conclusion in caveats. Write numbers as the tools stated them; do not quote rule-of-thumb thresholds as numbers.\n\n# Datasets\nPass a dataset's id as the `dataset` argument.\n{\n \"id\": \"FLCensusVTD\",\n \"rows\": 8871,\n \"description\": \"Data file FLCensusVTD.csv of this question; the question's data description is the description of dataset 'FLVoters'.\",\n \"columns\": {\n  \"county\": \"int64\",\n  \"VTD\": \"int64\",\n  \"total.pop\": \"int64\",\n  \"white\": \"float64\",\n  \"black\": \"float64\",\n  \"hispanic\": \"float64\",\n  \"api\": \"float64\",\n  \"others\": \"float64\"\n }\n}\n{\n \"id\": \"FLVoters\",\n \"rows\": 10000,\n \"description\": \"Here is a random sample of 10,000 registered voters from Florida contained in the CSV file FLVoters.csv.\\nVariable Description\\nsurname: surname\\ncounty: county ID of the voter’s residence\\nVTD: voting district ID of the voter’s residence\\nage: age\\ngender: gender m = male and f = female\\nrace: self-reported race\\n\\nThe US Census Bureau releases a list of common surnames with their frequency. For example, the most common surname was “Smith” with 2,376,206 occurrences, followed by “Johnson” and “Williams” with 1,857,160 and 1,534,042, respectively. This data set is quite comprehensive, including a total of more than 150,000 surnames that occurred at least 100 times. In addition, the census provides the relative frequencies of individual race within each surname, using a six-category self-reported race measure: non-Hispanic white, non-Hispanic black, non-Hispanic Asian and Pacific Islander, Hispanic origin, non-Hispanic American Indian and Alaskan Native, and non-Hispanic of two or more races. This census name list is contained in the CSV data file names.csv.\\nVariable Description\\nsurname: surname\\ncount: number of individuals with a specific surname\\npctwhite: percentage of non-Hispanic whites among those who have a specific surname\\npctblack: percentage of non-Hispanic blacks among those who have a specific surname\\npctapi: percentage of non-Hispanic Asians and Pacific Islanders among those who have a specific surname\\npcthispanic: percentage of Hispanic origin among those who have a specific surname\\npctothers: percentage of the other racial groups among those who have a specific surname\\n\\nIn the United States, voter files contain voters’ addresses. Using this information, our data set also provides the voting district where each voter lives. In addition, we will utilize the Florida census data, which contains the racial composition of each voting district. The census data set is in FLCensusVTD.csv.\\nVariable Description\\ncounty: county census ID of the voting district\\nVTD: voting district census ID (only unique within the county)\\ntotal.pop: total population of the voting district\\nwhite: proportion of non-Hispanic whites in the voting district\\nblack: proportion of non-Hispanic blacks in the voting district\\napi: proportion of non-Hispanic Asians and Pacific Islanders in the voting district\\nhispanic: proportion of voters of Hispanic origin in the voting district\\nothers: proportion of the other racial groups in the voting district\",\n \"columns\": {\n  \"surname\": \"str\",\n  \"county\": \"int64\",\n  \"VTD\": \"int64\",\n  \"age\": \"float64\",\n  \"gender\": \"str\",\n  \"race\": \"str\"\n }\n}\n{\n \"id\": \"names\",\n \"rows\": 151671,\n \"description\": \"Data file names.csv of this question; the question's data description is the description of dataset 'FLVoters'.\",\n \"columns\": {\n  \"surname\": \"str\",\n  \"count\": \"int64\",\n  \"pctwhite\": \"float64\",\n  \"pctblack\": \"float64\",\n  \"pctapi\": \"float64\",\n  \"pcthispanic\": \"float64\",\n  \"pctothers\": \"float64\"\n }\n}\n\n# Question\nWhat proportion of voters whose races are black are correctly classified in their racial category? Race is considered correctly classified if the racial category with the greatest conditional probability P(race | surname, residence) is identical to the self-reported race. Please remove those voters who contain at least a missing value. Please round to the nearest hundredth.","response":"submit_answer({\"answer\": \"Pelas regras deste roteador não identifiquei um desenho que responda à pergunta; nenhuma estimativa foi feita.\", \"figure_refs\": [], \"caveats\": []})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_16ddc6be","text":"","tool_calls":[{"vendor_id":"baseline_answer","llm_name":"submit_answer","tool_id":null,"args":{"answer":"Pelas regras deste roteador não identifiquei um desenho que responda à pergunta; nenhuma estimativa foi feita.","figure_refs":[],"caveats":[]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"0bed69bc34d9fdc2dd42ce3d75d4a505a540e2bc2c445b96ac05b626de9e57ce","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["df18e70deee6f243a72c24b6b383a2d5c6c06560f7c0387dd226b40714ce2be0"],"config":{}},"attempts":[],"response_id":null,"finish_reason":"tool_use","model_reported":"rules-1","usage":{"input":0,"output":0,"thoughts":0,"cached":0},"thoughts":null,"vendor":false,"wall_ms":0.328791793435812}}
{"call_id":"call_ba41dceb","run_id":"20261015T170025Z-suite-qrdata_socsci-baseline-2a40bf/qrdata_socsci.409/e1a1","step":"analysis","kind":"note","name":"analyst.guard","version":"","code_version":"62cdbe0860ad","inputs":{},"input_digest":"","dataset_digests":{},"result":null,"outputs":null,"output_digest":"","params_versions":[],"sources":[],"started_at":"2026-10-15T17:00:27.142432Z","duration_ms":0.0,"error":null,"decided_by":"code","rationale":"","cached":false,"informed_by":[],"model":null,"prompt":null,"response":null,"tokens_in":null,"tokens_out":null,"meta":{"turn_id":"call_16ddc6be","numbers_found":[],"unbacked":[],"uncited":[],"invalid_refs":[],"empty":false,"verdict":"accepted","retries_left":1,"vendor_id":"baseline_answer"}}