{"call_id":"call_b628a71c","run_id":"20261015T171401Z-suite-tool_selection-baseline-a1426d/tool_selection.pooling.test.s1000/e1a1","step":"analysis","kind":"llm","name":"llm.analyst.baseline","version":"","code_version":"2925aeab2262","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":"2026-10-15T17:14:02.806309Z","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\nNo dataset comes with this question: the numbers you need are in the question.\n\n# Question\nQueremos resumir a evidência sobre o efeito de creches públicas na participação das mães no mercado de trabalho, em pontos percentuais. Os estudos independentes que encontramos são:\n- Estudo A (2008): 4,6 (erro-padrão 2,2)\n- Estudo B (2011): 1,8 (erro-padrão 1,1)\n- Estudo C (2012): 1,0 (erro-padrão 2,4)\n- Estudo D (2013): 1,9 (erro-padrão 2,0)\n- Estudo E (2017): 2,0 (erro-padrão 1,1)\n- Estudo F (2018): 3,1 (erro-padrão 1,0)\nQual o efeito combinado?","response":"Vários estudos do mesmo efeito: meta-análise.\nevidence__pool_dl({\"estimates\": [{\"id\": \"e1\", \"study_id\": \"a\", \"estimate\": 4.6, \"se\": 2.2}, {\"id\": \"e2\", \"study_id\": \"b\", \"estimate\": 1.8, \"se\": 1.1}, {\"id\": \"e3\", \"study_id\": \"c\", \"estimate\": 1.0, \"se\": 2.4}, {\"id\": \"e4\", \"study_id\": \"d\", \"estimate\": 1.9, \"se\": 2.0}, {\"id\": \"e5\", \"study_id\": \"e\", \"estimate\": 2.0, \"se\": 1.1}, {\"id\": \"e6\", \"study_id\": \"f\", \"estimate\": 3.1, \"se\": 1.0}], \"unit\": \"pontos percentuais\"})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_b628a71c","text":"Vários estudos do mesmo efeito: meta-análise.","tool_calls":[{"vendor_id":"baseline_1","llm_name":"evidence__pool_dl","tool_id":"evidence.pool_dl","args":{"estimates":[{"id":"e1","study_id":"a","estimate":4.6,"se":2.2},{"id":"e2","study_id":"b","estimate":1.8,"se":1.1},{"id":"e3","study_id":"c","estimate":1.0,"se":2.4},{"id":"e4","study_id":"d","estimate":1.9,"se":2.0},{"id":"e5","study_id":"e","estimate":2.0,"se":1.1},{"id":"e6","study_id":"f","estimate":3.1,"se":1.0}],"unit":"pontos percentuais"}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"5ec81d84347bfcd291f77c36ebef99cc5836d341cbb25f78c669beedae0dcf8d","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["073dcd1857aeb4f6d53f643138b6200db60db4ce1c9b8ad3a577bc0fe0397540"],"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":0.8619579020887613}}
{"call_id":"call_9d562f6c","run_id":"20261015T171401Z-suite-tool_selection-baseline-a1426d/tool_selection.pooling.test.s1000/e1a1","step":"analysis","kind":"tool","name":"evidence.pool_dl","version":"0.1.0","code_version":"2925aeab2262","inputs":{"estimates":[{"id":"e1","study_id":"a","estimate":4.6,"se":2.2,"ci_low":null,"ci_high":null,"ci_level":0.95},{"id":"e2","study_id":"b","estimate":1.8,"se":1.1,"ci_low":null,"ci_high":null,"ci_level":0.95},{"id":"e3","study_id":"c","estimate":1.0,"se":2.4,"ci_low":null,"ci_high":null,"ci_level":0.95},{"id":"e4","study_id":"d","estimate":1.9,"se":2.0,"ci_low":null,"ci_high":null,"ci_level":0.95},{"id":"e5","study_id":"e","estimate":2.0,"se":1.1,"ci_low":null,"ci_high":null,"ci_level":0.95},{"id":"e6","study_id":"f","estimate":3.1,"se":1.0,"ci_low":null,"ci_high":null,"ci_level":0.95}],"unit":"pontos percentuais","level":0.95},"input_digest":"1b8afd7e31ee58a956cf184cb328a47ee7ea6ff0a5364b9abed49ed7d0734dd6","dataset_digests":{},"result":{"tool":"evidence.pool_dl","version":"0.1.0","call_id":"call_9d562f6c","status":"ok","reason":"","figures":[{"key":"pooled","value":2.3878297351630104,"unit":"pontos percentuais","label":"Random-effects pooled estimate (DerSimonian–Laird) over 6 studies","se":0.5518956058203403,"ci_low":1.3061342245292293,"ci_high":3.469525245796792,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Cada um dos 6 estudos entra com uma estimativa y e o seu erro-padrão ep (derivado do intervalo publicado, ao nível que o estudo declara, quando o erro-padrão não é publicado); o peso de efeitos fixos é o inverso da variância.","formula":"w_1 = 1 / ep_1² ; w_2 = 1 / ep_2² ; w_3 = 1 / ep_3² ; w_4 = 1 / ep_4² ; w_5 = 1 / ep_5² ; w_6 = 1 / ep_6²","terms":[{"symbol":"y_1","value":4.6,"unit":"pontos percentuais","note":"a (e1)"},{"symbol":"ep_1","value":2.2,"unit":"pontos percentuais","note":"erro-padrão publicado"},{"symbol":"w_1","value":0.20661157024793386,"unit":"","note":""},{"symbol":"y_2","value":1.8,"unit":"pontos percentuais","note":"b (e2)"},{"symbol":"ep_2","value":1.1,"unit":"pontos percentuais","note":"erro-padrão publicado"},{"symbol":"w_2","value":0.8264462809917354,"unit":"","note":""},{"symbol":"y_3","value":1.0,"unit":"pontos percentuais","note":"c (e3)"},{"symbol":"ep_3","value":2.4,"unit":"pontos percentuais","note":"erro-padrão publicado"},{"symbol":"w_3","value":0.1736111111111111,"unit":"","note":""},{"symbol":"y_4","value":1.9,"unit":"pontos percentuais","note":"d (e4)"},{"symbol":"ep_4","value":2.0,"unit":"pontos percentuais","note":"erro-padrão publicado"},{"symbol":"w_4","value":0.25,"unit":"","note":""},{"symbol":"y_5","value":2.0,"unit":"pontos percentuais","note":"e (e5)"},{"symbol":"ep_5","value":1.1,"unit":"pontos percentuais","note":"erro-padrão publicado"},{"symbol":"w_5","value":0.8264462809917354,"unit":"","note":""},{"symbol":"y_6","value":3.1,"unit":"pontos percentuais","note":"f (e6)"},{"symbol":"ep_6","value":1.0,"unit":"pontos percentuais","note":"erro-padrão publicado"},{"symbol":"w_6","value":1.0,"unit":"","note":""}],"result":null,"unit":""},{"text_pt":"A média de efeitos fixos pondera cada estudo pela sua precisão.","formula":"ȳ_F = (w_1·y_1 + w_2·y_2 + w_3·y_3 + w_4·y_4 + w_5·y_5 + w_6·y_6) / (w_1 + w_2 + w_3 + w_4 + w_5 + w_6)","terms":[],"result":2.3878297351630104,"unit":"pontos percentuais"},{"text_pt":"A estatística Q mede quanto os estudos discordam da média de efeitos fixos além do acaso.","formula":"Q = w_1·(y_1 − ȳ_F)² + w_2·(y_2 − ȳ_F)² + w_3·(y_3 − ȳ_F)² + w_4·(y_4 − ȳ_F)² + w_5·(y_5 − ȳ_F)² + w_6·(y_6 − ȳ_F)²","terms":[],"result":2.32204356414259,"unit":""},{"text_pt":"C é a constante de escala de DerSimonian e Laird.","formula":"C = (w_1 + w_2 + w_3 + w_4 + w_5 + w_6) − (w_1² + w_2² + w_3² + w_4² + w_5² + w_6²) / (w_1 + w_2 + w_3 + w_4 + w_5 + w_6)","terms":[],"result":2.521230300477982,"unit":""},{"text_pt":"A variância entre estudos τ² é zero quando Q não excede k − 1, o que o acaso sozinho produziria.","formula":"τ² = max(0, (Q − (k − 1)) / C)","terms":[{"symbol":"k","value":6.0,"unit":"estudos","note":""}],"result":0.0,"unit":"(pontos percentuais)²"},{"text_pt":"I² é a parcela da dispersão observada que vem da variação entre estudos, não do acaso.","formula":"I² = max(0, (Q − (k − 1)) / Q) × 100","terms":[],"result":0.0,"unit":"%"},{"text_pt":"Os pesos de efeitos aleatórios somam τ² à variância de cada estudo, o que aproxima os pesos entre si quando os estudos discordam.","formula":"w*_1 = 1 / (ep_1² + τ²) ; w*_2 = 1 / (ep_2² + τ²) ; w*_3 = 1 / (ep_3² + τ²) ; w*_4 = 1 / (ep_4² + τ²) ; w*_5 = 1 / (ep_5² + τ²) ; w*_6 = 1 / (ep_6² + τ²)","terms":[{"symbol":"w*_1","value":0.20661157024793386,"unit":"","note":""},{"symbol":"w*_2","value":0.8264462809917354,"unit":"","note":""},{"symbol":"w*_3","value":0.1736111111111111,"unit":"","note":""},{"symbol":"w*_4","value":0.25,"unit":"","note":""},{"symbol":"w*_5","value":0.8264462809917354,"unit":"","note":""},{"symbol":"w*_6","value":1.0,"unit":"","note":""}],"result":null,"unit":""},{"text_pt":"O erro-padrão da média agrupada vem da soma dos pesos de efeitos aleatórios.","formula":"ep(ȳ) = √(1 / (w*_1 + w*_2 + w*_3 + w*_4 + w*_5 + w*_6))","terms":[],"result":0.5518956058203403,"unit":"pontos percentuais"},{"text_pt":"A média agrupada é a média das estimativas ponderada pelos pesos de efeitos aleatórios.","formula":"ȳ = (w*_1·y_1 + w*_2·y_2 + w*_3·y_3 + w*_4·y_4 + w*_5·y_5 + w*_6·y_6) / (w*_1 + w*_2 + w*_3 + w*_4 + w*_5 + w*_6)","terms":[],"result":2.3878297351630104,"unit":"pontos percentuais"},{"text_pt":"O intervalo de 95% usa o valor crítico da normal.","formula":"IC95 = ȳ ± z·ep(ȳ)","terms":[{"symbol":"z","value":1.959963984540054,"unit":"","note":"quantil 0.975 da normal padrão"}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"k","value":6,"flag":false,"note":"independent studies"},{"name":"tau2","value":0.0,"flag":false,"note":"between-study variance, (pontos percentuais)²"},{"name":"i2","value":0.0,"flag":false,"note":"% of the spread that is between studies"},{"name":"q","value":2.32204356414259,"flag":false,"note":"Cochran's Q"},{"name":"n_se_from_interval","value":0,"flag":false,"note":"standard errors taken from published intervals"}],"tables":{"studies":[{"id":"e1","study_id":"a","estimate":4.6,"se":2.2,"se_source":"reported","weight_fixed":0.20661157024793386,"weight_random":0.20661157024793386,"weight_pct":6.293156192640503},{"id":"e2","study_id":"b","estimate":1.8,"se":1.1,"se_source":"reported","weight_fixed":0.8264462809917354,"weight_random":0.8264462809917354,"weight_pct":25.172624770562013},{"id":"e3","study_id":"c","estimate":1.0,"se":2.4,"se_source":"reported","weight_fixed":0.1736111111111111,"weight_random":0.1736111111111111,"weight_pct":5.287999300760423},{"id":"e4","study_id":"d","estimate":1.9,"se":2.0,"se_source":"reported","weight_fixed":0.25,"weight_random":0.25,"weight_pct":7.61471899309501},{"id":"e5","study_id":"e","estimate":2.0,"se":1.1,"se_source":"reported","weight_fixed":0.8264462809917354,"weight_random":0.8264462809917354,"weight_pct":25.172624770562013},{"id":"e6","study_id":"f","estimate":3.1,"se":1.0,"se_source":"reported","weight_fixed":1.0,"weight_random":1.0,"weight_pct":30.45887597238004}]},"datasets":{},"warnings":[],"assumptions":["every estimate measures the same effect in 'pontos percentuais'","each study contributes one independent estimate"],"notes":""},"outputs":null,"output_digest":"9d42728fa3a3ad9aa3226479720bd857440d838b8f51ec8ee262628ed5c4752b","params_versions":[],"sources":[],"started_at":"2026-10-15T17:14:02.809006Z","duration_ms":0.3009159117937088,"error":null,"decided_by":"model","rationale":"Vários estudos do mesmo efeito: meta-análise.","cached":false,"informed_by":[],"model":null,"prompt":null,"response":null,"tokens_in":null,"tokens_out":null,"meta":{"turn_id":"call_b628a71c","llm_name":"evidence__pool_dl","vendor_id":"baseline_1","result_for_model_ref":"4bc8e67461cff7b8ea33392c169e80b166085dc8ce6ab45e1feb4e53bf869fb6"}}
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