{"attempt":1,"datasets":{"d":{"description_shown":"NIST StRD SmLs03: 18009 observations; treatment = the treatment level (1 to 9), response = the response, treatment_2 \u2026 treatment_9 = 1 when the row is at that treatment level (level 1 is the reference)","file_sha256":"eb23c78904a4d0a238a144503a4f7db7eb849be21937ef1e20f1dbff2d264355","frame_sha256":null,"role":"agent","transforms":[{"args":{"column":"treatment","drop_first":true,"prefix":"treatment"},"op":"dummies"}]}},"epoch":1,"event":"sample_init","question":"NIST Statistical Reference Datasets, one-way analysis of variance, problem SmLs03: generated data with 1 constant leading digits; one factor (treatment) with 9 levels and 18009 observations in a balanced design, model y_ij = mu + tau_i + e_ij. Dataset d holds the factor treatment (levels 1 to 9), the response response, and indicator columns treatment_2 to treatment_9 (1 when the row is at that level; level 1 is the reference). Compute the ANOVA F statistic for the hypothesis that all 9 treatment effects are equal: the between-treatment mean square divided by the within-treatment mean square.","question_sha256":"6f2bf4fffe9e5a5760ad4d9c1bbc1aebea65aa823365f0e1d2de59d7af33fbd2","run_id":"20261015T171223Z-suite-nist_anova-scripted-7361e5","seq":0,"skills":[],"task_id":"nist_anova.smls03.agent","task_sha256":"ee54aa7517c43ba58b09ad6117e9e16ea94247a304e79392c89b685f8ba04cf2","timestamp":"2026-10-15T17:12:23.506377Z"}
{"attempt":1,"call_id":"call_0d5c500c","dataset_id":"d","epoch":1,"event":"transform","frame_sha256_after":"215a38263b0761fdb14d43943252b3e81604c46f1c8220b30b2a5e0e04db5593","frame_sha256_before":"45db46fdb9a12736238f8fa6c75b5cce9bd0faf7d0fd4b00256ccc9aa25ee7df","index":0,"rows_after":18009,"rows_before":18009,"run_id":"20261015T171223Z-suite-nist_anova-scripted-7361e5","seq":1,"task_id":"nist_anova.smls03.agent","timestamp":"2026-10-15T17:12:23.506377Z","transform":{"args":{"column":"treatment","drop_first":true,"prefix":"treatment"},"op":"dummies"}}
{"attempt":1,"attempts":[],"call_id":"call_6d6afcf4","epoch":1,"event":"model","finish_reason":"tool_use","latency_ms":0.0,"model_reported":"reference","model_requested":"reference","provider":"scripted","request":{"config":{},"contents_refs":[],"messages_refs":["cd3253465d6761cc9505175a5189f1b9665c6d7dc01031fb7f7b84876774b518"],"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"1f99f36ee436b4adef1f4ef2806080532383d9de4d815fa11951bffc9c4153e9","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b"},"response":{"text":"With classical errors, the joint test that every indicator's coefficient is zero is the one-way ANOVA F.","thoughts":null,"tool_calls":[{"args":{"dataset":"d","hypothesis":"treatment_2 = 0, treatment_3 = 0, treatment_4 = 0, treatment_5 = 0, treatment_6 = 0, treatment_7 = 0, treatment_8 = 0, treatment_9 = 0","se":"classical","x":["treatment_2","treatment_3","treatment_4","treatment_5","treatment_6","treatment_7","treatment_8","treatment_9"],"y":"response"},"llm_name":"infer__linear_hypothesis","tool_id":"infer.linear_hypothesis","vendor_id":"script_1"}]},"response_id":null,"run_id":"20261015T171223Z-suite-nist_anova-scripted-7361e5","seq":2,"task_id":"nist_anova.smls03.agent","timestamp":"2026-10-15T17:12:23.524863Z","usage":{"cached":0,"input":0,"output":0,"thoughts":0},"vendor":false}
{"arguments":{"cluster":null,"dataset":"d","hypothesis":"treatment_2 = 0, treatment_3 = 0, treatment_4 = 0, treatment_5 = 0, treatment_6 = 0, treatment_7 = 0, treatment_8 = 0, treatment_9 = 0","se":"classical","subset":null,"x":["treatment_2","treatment_3","treatment_4","treatment_5","treatment_6","treatment_7","treatment_8","treatment_9"],"y":"response"},"attempt":1,"cached":false,"call_id":"call_92aa4938","card_version":"0.1.0","code_version":"2925aeab2262","dataset_digests":{"d":"215a38263b0761fdb14d43943252b3e81604c46f1c8220b30b2a5e0e04db5593"},"decided_by":"model","duration_ms":28.44666689634323,"epoch":1,"error":null,"event":"tool","informed_by":[],"input_digest":"d9ac946eac7ed4017b491543734b9be5b075b0a8da2ae4dabbdd79d85f56b823","llm_name":"infer__linear_hypothesis","output_digest":"bf64818f798de94096dc9267623015247588f1207d3b82bac4effb1e28720ca3","rationale":"With classical errors, the joint test that every indicator's coefficient is zero is the one-way ANOVA F.","replay_class":"replayed","result":{"assumptions":["errors are independent and have the same variance for every observation"],"call_id":"call_92aa4938","datasets":{},"diagnostics":[{"flag":false,"name":"statistic","note":"F = W/m against F(m, n \u2212 k)","value":"F"},{"flag":false,"name":"df_numerator","note":"number of restrictions","value":8},{"flag":false,"name":"df_denominator","note":"residual degrees of freedom n \u2212 k","value":18000},{"flag":false,"name":"se_type","note":"classical (homoskedastic) standard errors","value":"classical"},{"flag":false,"name":"n_obs","note":"","value":18009},{"flag":false,"name":"n_clusters","note":"errors not clustered","value":null}],"figures":[{"ci_high":null,"ci_level":null,"ci_low":null,"derivation":{"note_pt":"","steps":[{"formula":"","result":16008.00000000039,"terms":[{"note":"","symbol":"m","unit":"restri\u00e7\u00f5es","value":8.0}],"text_pt":"Estat\u00edstica de Wald das 8 restri\u00e7\u00f5es, com a covari\u00e2ncia classical (homoskedastic) standard errors.","unit":""},{"formula":"F = W / m","result":2001.0000000000487,"terms":[{"note":"","symbol":"W","unit":"","value":16008.00000000039},{"note":"","symbol":"m","unit":"restri\u00e7\u00f5es","value":8.0}],"text_pt":"Estat\u00edstica F: a de Wald dividida pelo n\u00famero de restri\u00e7\u00f5es.","unit":""}]},"key":"F","label":"F statistic of treatment_2 = 0; treatment_3 = 0; treatment_4 = 0; treatment_5 = 0; treatment_6 = 0; treatment_7 = 0; treatment_8 = 0; treatment_9 = 0","se":null,"unit":"","value":2001.0000000000487},{"ci_high":null,"ci_level":null,"ci_low":null,"derivation":{"note_pt":"","steps":[{"formula":"","result":0.0,"terms":[],"text_pt":"Probabilidade de uma F(8, 18000) exceder a estat\u00edstica observada.","unit":"probabilidade"}]},"key":"p_value","label":"p-value of treatment_2 = 0; treatment_3 = 0; treatment_4 = 0; treatment_5 = 0; treatment_6 = 0; treatment_7 = 0; treatment_8 = 0; treatment_9 = 0","se":null,"unit":"probability","value":0.0}],"notes":"","reason":"","status":"ok","tables":{"coefficients":[{"ci_high":1.4043818121039973,"ci_low":1.3956181878960026,"coef":1.4,"p":0.0,"se":0.002235509170049368,"t":626.2555388976924,"term":"Intercept"},{"ci_high":-0.09380318189475649,"ci_low":-0.10619681810524369,"coef":-0.10000000000000009,"p":9.559128140673934e-214,"se":0.0031614873870932677,"t":-31.630681307870727,"term":"treatment_2"},{"ci_high":0.10619681810524341,"ci_low":0.09380318189475655,"coef":0.09999999999999998,"p":9.559128140429405e-214,"se":0.003161487387093179,"t":31.63068130787158,"term":"treatment_3"},{"ci_high":-0.09380318189475685,"ci_low":-0.10619681810524333,"coef":-0.10000000000000009,"p":9.55912814013987e-214,"se":0.0031614873870930817,"t":-31.63068130787259,"term":"treatment_4"},{"ci_high":0.1061968181052438,"ci_low":0.09380318189475616,"coef":0.09999999999999998,"p":9.559128141008471e-214,"se":0.003161487387093381,"t":31.63068130786956,"term":"treatment_5"},{"ci_high":-0.09380318189475624,"ci_low":-0.10619681810524394,"coef":-0.10000000000000009,"p":9.559128141036975e-214,"se":0.003161487387093394,"t":-31.630681307869462,"term":"treatment_6"},{"ci_high":0.10619681810524394,"ci_low":0.09380318189475602,"coef":0.09999999999999998,"p":9.559128141200495e-214,"se":0.0031614873870934477,"t":31.63068130786889,"term":"treatment_7"},{"ci_high":-0.09380318189475642,"ci_low":-0.10619681810524376,"coef":-0.10000000000000009,"p":9.559128140774446e-214,"se":0.003161487387093303,"t":-31.630681307870375,"term":"treatment_8"},{"ci_high":0.10619681810524356,"ci_low":0.0938031818947564,"coef":0.09999999999999998,"p":9.559128140666433e-214,"se":0.003161487387093262,"t":31.63068130787075,"term":"treatment_9"}],"restrictions":[{"estimate":-0.10000000000000009,"hypothesized":0.0,"restriction":"treatment_2 = 0"},{"estimate":0.09999999999999998,"hypothesized":0.0,"restriction":"treatment_3 = 0"},{"estimate":-0.10000000000000009,"hypothesized":0.0,"restriction":"treatment_4 = 0"},{"estimate":0.09999999999999998,"hypothesized":0.0,"restriction":"treatment_5 = 0"},{"estimate":-0.10000000000000009,"hypothesized":0.0,"restriction":"treatment_6 = 0"},{"estimate":0.09999999999999998,"hypothesized":0.0,"restriction":"treatment_7 = 0"},{"estimate":-0.10000000000000009,"hypothesized":0.0,"restriction":"treatment_8 = 0"},{"estimate":0.09999999999999998,"hypothesized":0.0,"restriction":"treatment_9 = 0"}]},"tool":"infer.linear_hypothesis","version":"0.1.0","warnings":[]},"result_for_model_ref":"e2ddc8eb64e926b8340042011c20b65af250d482ccdc5e40573188244a14683f","result_sha256":"5abe7b0d0850e1358b2283900bc31856dca86e055dec909970634182b1e76da3","run_id":"20261015T171223Z-suite-nist_anova-scripted-7361e5","seq":3,"synthetic":false,"task_id":"nist_anova.smls03.agent","timestamp":"2026-10-15T17:12:23.527130Z","tool_id":"infer.linear_hypothesis","turn_id":"call_6d6afcf4"}
{"attempt":1,"attempts":[],"call_id":"call_2ef81fd6","epoch":1,"event":"model","finish_reason":"tool_use","latency_ms":0.0,"model_reported":"reference","model_requested":"reference","provider":"scripted","request":{"config":{},"contents_refs":[],"messages_refs":["cd3253465d6761cc9505175a5189f1b9665c6d7dc01031fb7f7b84876774b518","ea561c2acfc25b0d50a3954e14b116d696afca6de808dcdf3eb659b3405e30a5","f86c65446c973bec85d26ae03271e2c675bc6ff331061fbd876c484ac2d74fef"],"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"1f99f36ee436b4adef1f4ef2806080532383d9de4d815fa11951bffc9c4153e9","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b"},"response":{"text":"","thoughts":null,"tool_calls":[{"args":{"answer":"Estimativa (infer.linear_hypothesis): F = 2001.","caveats":[],"figure_refs":["call_92aa4938:F"]},"llm_name":"submit_answer","tool_id":null,"vendor_id":"script_2"}]},"response_id":null,"run_id":"20261015T171223Z-suite-nist_anova-scripted-7361e5","seq":4,"task_id":"nist_anova.smls03.agent","timestamp":"2026-10-15T17:12:23.555817Z","usage":{"cached":0,"input":0,"output":0,"thoughts":0},"vendor":false}
{"attempt":1,"call_id":"call_cc15fe7e","epoch":1,"event":"guard","numbers_found":["2001"],"retries_left":1,"run_id":"20261015T171223Z-suite-nist_anova-scripted-7361e5","seq":5,"task_id":"nist_anova.smls03.agent","timestamp":"2026-10-15T17:12:23.557361Z","turn_id":"call_2ef81fd6","unbacked":[],"uncited":[],"verdict":"accepted"}
{"answer":"Estimativa (infer.linear_hypothesis): F = 2001.","answer_figure":{"call_id":"call_92aa4938","ci_high":null,"ci_low":null,"formatted":2001.0000000000487,"key":"F","ref":"call_92aa4938:F","se":null,"tool_id":"infer.linear_hypothesis","unit":"","value":2001.0000000000487},"attempt":1,"caveats":[],"epoch":1,"event":"answer","figure_refs":["call_92aa4938:F"],"run_id":"20261015T171223Z-suite-nist_anova-scripted-7361e5","seq":6,"stopped":"answered","task_id":"nist_anova.smls03.agent","timestamp":"2026-10-15T17:12:23.557561Z"}
{"attempt":1,"check":{"check_index":0,"detail":"every number backed","expected":null,"failure_class":null,"kind":"answer_backed","observed":[],"passed":true,"required":true,"source_id":null,"tolerance":null,"trace_call_id":null,"weight":1.0},"epoch":1,"event":"score","run_id":"20261015T171223Z-suite-nist_anova-scripted-7361e5","seq":7,"task_id":"nist_anova.smls03.agent","timestamp":"2026-10-15T17:12:23.557561Z"}
{"attempt":1,"check":{"check_index":1,"detail":"cited ['call_92aa4938:F']","expected":null,"failure_class":null,"kind":"answer_cites","observed":["call_92aa4938:F"],"passed":true,"required":true,"source_id":null,"tolerance":null,"trace_call_id":"call_92aa4938","weight":1.0},"epoch":1,"event":"score","run_id":"20261015T171223Z-suite-nist_anova-scripted-7361e5","seq":8,"task_id":"nist_anova.smls03.agent","timestamp":"2026-10-15T17:12:23.557561Z"}
{"attempt":1,"check":{"check_index":2,"detail":"answer call is infer.linear_hypothesis","expected":null,"failure_class":null,"kind":"tool_called","observed":"infer.linear_hypothesis","passed":true,"required":true,"source_id":null,"tolerance":null,"trace_call_id":"call_92aa4938","weight":1.0},"epoch":1,"event":"score","run_id":"20261015T171223Z-suite-nist_anova-scripted-7361e5","seq":9,"task_id":"nist_anova.smls03.agent","timestamp":"2026-10-15T17:12:23.557561Z"}
{"attempt":1,"check":{"check_index":3,"detail":"infer.linear_hypothesis.se = 'classical'","expected":"classical","failure_class":null,"kind":"arg_equals","observed":"classical","passed":true,"required":true,"source_id":null,"tolerance":null,"trace_call_id":"call_92aa4938","weight":1.0},"epoch":1,"event":"score","run_id":"20261015T171223Z-suite-nist_anova-scripted-7361e5","seq":10,"task_id":"nist_anova.smls03.agent","timestamp":"2026-10-15T17:12:23.557561Z"}
{"attempt":1,"check":{"check_index":4,"detail":"8 vs 8","expected":8,"failure_class":null,"kind":"diagnostic","observed":8,"passed":true,"required":true,"source_id":null,"tolerance":{"abs_tol":null,"interval":null,"min_lre":null,"open_interval":false,"rel_tol":null,"rule":"default rel 1e-6 (abs floor 1e-6)"},"trace_call_id":"call_92aa4938","weight":1.0},"epoch":1,"event":"score","run_id":"20261015T171223Z-suite-nist_anova-scripted-7361e5","seq":11,"task_id":"nist_anova.smls03.agent","timestamp":"2026-10-15T17:12:23.557561Z"}
{"attempt":1,"check":{"check_index":5,"detail":"18000 vs 18000","expected":18000,"failure_class":null,"kind":"diagnostic","observed":18000,"passed":true,"required":true,"source_id":null,"tolerance":{"abs_tol":null,"interval":null,"min_lre":null,"open_interval":false,"rel_tol":null,"rule":"default rel 1e-6 (abs floor 1e-6)"},"trace_call_id":"call_92aa4938","weight":1.0},"epoch":1,"event":"score","run_id":"20261015T171223Z-suite-nist_anova-scripted-7361e5","seq":12,"task_id":"nist_anova.smls03.agent","timestamp":"2026-10-15T17:12:23.557561Z"}
{"attempt":1,"check":{"check_index":6,"detail":"LRE 13.61 (min 9.0)","expected":2001.0,"failure_class":null,"kind":"figure_lre","observed":13.614,"passed":true,"required":true,"source_id":"nist_anova.smls03.f","tolerance":{"abs_tol":null,"interval":null,"min_lre":9.0,"open_interval":false,"rel_tol":null,"rule":"LRE >= 9.0"},"trace_call_id":"call_92aa4938","weight":1.0},"epoch":1,"event":"score","run_id":"20261015T171223Z-suite-nist_anova-scripted-7361e5","seq":13,"task_id":"nist_anova.smls03.agent","timestamp":"2026-10-15T17:12:23.557561Z"}
