{"attempt":1,"datasets":{"d":{"description_shown":"NIST StRD PiDigits: 5000 observations of y, one per row","file_sha256":"e9421a2f4c0a31c505099332b4d22e507e808953c3c57bddae9226f25587d7d2","frame_sha256":null,"role":"agent","transforms":[]}},"epoch":1,"event":"sample_init","question":"NIST Statistical Reference Datasets, univariate summary statistics, problem PiDigits: the first 5000 digits of the mathematical constant pi, one digit per observation. NIST uses this data set to test the accuracy of summary statistics calculations. Dataset d holds the 5000 observations in column y. Compute the sample mean of y and the sample standard deviation of y (denominator n \u2212 1).","question_sha256":"481fdf19ec74ae37b00ac1d22fe7660fd93f1a7ff274bd27bcd6d0d6053c1704","run_id":"20261015T124724Z-suite-nist_univariate-gemini-28801c","seq":0,"skills":[],"task_id":"nist_univariate.pidigits.agent","task_sha256":"d660b6ba1eba0c4e1998e7923d38dc641c1f7f4e393ade4ed0f5d77155ebd34d","timestamp":"2026-10-15T12:47:24.679555Z"}
{"attempt":1,"attempts":[{"config_ref":"8ee0d84c29b117269608a8edad648afadcb20e12f00a5d88b7d64223f4b057e2","discarded":false,"error":null,"finish_reason":"STOP","latency_ms":8282.164999982342,"raw_ref":"df226effab6f43475fcf1c1a3ab09b1387730de14efcbf693e36fc2c201acd9f","started_at":"2026-10-15T12:47:24.907698Z","usage":{"cached":0,"input":18125,"output":41,"thoughts":259}}],"call_id":"call_546086f9","epoch":1,"event":"model","finish_reason":"STOP","latency_ms":8282.164999982342,"model_reported":"gemini-3.8-flash","model_requested":"gemini-3.8-flash","provider":"gemini","request":{"config":{"automatic_function_calling":{"disable":true,"maximum_remote_calls":10},"max_output_tokens":32000,"seed":null,"temperature":null,"thinking_config":{"include_thoughts":true},"tool_config":{"function_calling_config":{"mode":"AUTO"}},"top_k":null,"top_p":null},"contents_refs":["33600144cd179b02900c44d943a84667c0634f564cacc67495784ccd3ddc7ae2"],"messages_refs":["ceebe6af7b998cb015e219f3fbe15b8de6b4e386a830d0c0e35830fb9f6df604"],"system_stable_ref":"265d63d091732725cd5b15f51e1686b68b6b276ae88794f6120dc818830a2a97","system_task_ref":"e5ade2fd09ab0ec6cf4558f7c6c416c2baf82148edafe5f60d20cb4590719a12","tools_ref":"a44ef51f03df5188f084f1e0227b68df116b942a22fd50c0a88670cf3461fedd"},"response":{"text":"I will describe the column y in dataset d to obtain its sample mean and sample standard deviation.","thoughts":"**My Approach to Calculating Sample Statistics**\n\nOkay, I need to find the sample mean and sample standard deviation (using the n-1 denominator, of course) for the 'y' column in this NIST StRD PiDigits dataset 'd'. Let's think through the best approach here. My first instinct is to see what tools are available for this kind of computation. I immediately think of inspecting the `data__describe` function. It seems like the most obvious candidate for getting descriptive stats quickly.\n\nLet me quickly remind myself of `data__describe`'s parameters; I need to supply the `dataset` and the `columns` I'm interested in, which in this case is just 'y'. Now, the crucial question: does `data__describe` compute the *sample* standard deviation? I'll need to check the function's documentation to confirm that it's using the correct formula.\n\nHold on, I also had a fleeting thought about `infer__ols`. I know that I *could* potentially use a regression to get the mean (by fitting a model with an intercept), although this seems like a somewhat roundabout method. Wait... I can't. `infer__ols` requires regressors (`x`) and I can't use the same column as both the response and regressor. Hmm, that's a dead end. Still, let me remind myself: OLS with intercept only could give the mean. But the primary goal is the sample SD, so back to `data__describe`.\n\nRight, the smart move is to use `data__describe`. I'll execute `data__describe(dataset=\"d\", columns=[\"y\"])` and hopefully I'll have the mean and sample standard deviation at my fingertips. Let's see...\n\n\n","tool_calls":[{"args":{"columns":["y"],"dataset":"d"},"llm_name":"data__describe","tool_id":"data.describe","vendor_id":"call_652179"}]},"response_id":"XcvQaqWBJaammtkPh6iC-A4","run_id":"20261015T124724Z-suite-nist_univariate-gemini-28801c","seq":1,"task_id":"nist_univariate.pidigits.agent","timestamp":"2026-10-15T12:47:24.679555Z","usage":{"cached":0,"input":18125,"output":41,"thoughts":259},"vendor":true}
{"arguments":{"columns":["y"],"dataset":"d"},"attempt":1,"cached":false,"call_id":"call_743b03b3","card_version":"0.1.0","code_version":"38e979372a0f","dataset_digests":{"d":"61b133b41a102950f9fd38f344de7406d711e7f29f403f0cd143de592f4a380a"},"decided_by":"model","duration_ms":0.7621250115334988,"epoch":1,"error":null,"event":"tool","informed_by":[],"input_digest":"1278a461223a3d94ef24a0e3284f78402986a034a9508d8a881b1ef9fb064481","llm_name":"data__describe","output_digest":"e7e59dfdf33e7c7b93b3c5988d7f007a1d27ac083075cce8a4d8f7479f7d920b","rationale":"I will describe the column y in dataset d to obtain its sample mean and sample standard deviation.","replay_class":"replayed","result":{"assumptions":[],"call_id":"call_743b03b3","datasets":{},"diagnostics":[],"figures":[{"ci_high":null,"ci_level":null,"ci_low":null,"derivation":null,"key":"n_rows","label":"Rows in the dataset","se":null,"unit":"rows","value":5000.0}],"notes":"","reason":"","status":"ok","tables":{"columns":[{"column":"y","dtype":"int64","max":9.0,"mean":4.5348,"min":0.0,"n_missing":0,"n_unique":10,"sd":2.867339060288708}]},"tool":"data.describe","version":"0.1.0","warnings":[]},"result_for_model_ref":"f26b24f6ca7f553fefd5f23cccea81f62bc2d04e76885dd89c4857b1dfa13a85","result_sha256":"cb45ffa0e250e61d86a101b711bb27f1e54643a6d56d80fa5e95cd416cf5dec2","run_id":"20261015T124724Z-suite-nist_univariate-gemini-28801c","seq":2,"synthetic":false,"task_id":"nist_univariate.pidigits.agent","timestamp":"2026-10-15T12:47:33.193677Z","tool_id":"data.describe","turn_id":"call_546086f9"}
{"attempt":1,"attempts":[{"config_ref":"8ee0d84c29b117269608a8edad648afadcb20e12f00a5d88b7d64223f4b057e2","discarded":false,"error":null,"finish_reason":"STOP","latency_ms":20919.86812511459,"raw_ref":"2286d08a0a01538b3ca54f37ba21ecd773a038eec9d290f14de447e1c4c6b4db","started_at":"2026-10-15T12:47:33.206275Z","usage":{"cached":16413,"input":18927,"output":94,"thoughts":2240}}],"call_id":"call_de08ba12","epoch":1,"event":"model","finish_reason":"STOP","latency_ms":20919.86812511459,"model_reported":"gemini-3.8-flash","model_requested":"gemini-3.8-flash","provider":"gemini","request":{"config":{"automatic_function_calling":{"disable":true,"maximum_remote_calls":10},"max_output_tokens":32000,"seed":null,"temperature":null,"thinking_config":{"include_thoughts":true},"tool_config":{"function_calling_config":{"mode":"AUTO"}},"top_k":null,"top_p":null},"contents_refs":["33600144cd179b02900c44d943a84667c0634f564cacc67495784ccd3ddc7ae2","a3858da8df67ddedd8c4f8207d89c80cedf3c11ddc4afd23c66da86ba55d3378","467e6da8a57cd5e2f8d10ee85fcb1575424b2f2ea77d2122ab02d8d3d8ae7354"],"messages_refs":["ceebe6af7b998cb015e219f3fbe15b8de6b4e386a830d0c0e35830fb9f6df604","0d9bab522587770770f0435f09872d464d36715f1b10efa595f6b1e0d9e879e7","5319577821e300ab890dcb29c0925b99331c0acf9680b31bc86f7b483bd4d88c"],"system_stable_ref":"265d63d091732725cd5b15f51e1686b68b6b276ae88794f6120dc818830a2a97","system_task_ref":"e5ade2fd09ab0ec6cf4558f7c6c416c2baf82148edafe5f60d20cb4590719a12","tools_ref":"a44ef51f03df5188f084f1e0227b68df116b942a22fd50c0a88670cf3461fedd"},"response":{"text":"","thoughts":"**Analysis of Univariate Statistics for Pi Digits Dataset**\n\nOkay, let's break this down step-by-step. I need to verify the univariate statistics for the PiDigits dataset. First, I checked `data.describe` and observed the mean of 'y' is 4.5348, the standard deviation is 2.86734, min is 0, max is 9, there are no missing values, and there are 10 unique values. I then confirmed these values against the NIST certified values for PiDigits. The output of `data.describe` appears correct.\n\nI need to confirm all required information for the final answer. I also need to make sure I meet the specific rules. Rules 1 and 7 and the overall logic need to be followed. Every number must originate from a tool call or the prompt, and every figure reference must exist.\n\nI realize that 'mean' and 'sd' are located within the table cells of the `data.describe` table, not in the `figures` output. However, the number of rows, 5000, is in the figures. I check what tools produce figures to be sure to follow rules. I see that the appropriate tool is `data.describe` as it's designed to provide univariate summaries. Since the question is in English, per the rules, I'll write the answer in English. The tools have formatted numbers with a decimal point. I will use 4.5348 and 2.86734 as the tool outputs indicate.\n\nFinally, I determine I must include `call_743b03b3:n_rows` in `figure_refs` since I will reference 5000 from the figures. There are no caveats. The data are complete, and the calculations are standard.\n\nSo, I will submit the following:\n```json\n{\n  \"answer\": \"The sample mean of column y is 4.5348 and the sample standard deviation is 2.86734. The dataset has 5000 observations.\",\n  \"figure_refs\": [\n    \"call_743b03b3:n_rows\"\n  ],\n  \"caveats\": []\n}\n```\n\n\n","tool_calls":[{"args":{"answer":"For the 5000 observations of the PiDigits dataset in column y, the sample statistics are:\n\n- Sample mean: 4.5348\n- Sample standard deviation (denominator n \u2212 1): 2.86734","caveats":[],"figure_refs":["call_743b03b3:n_rows"]},"llm_name":"submit_answer","tool_id":null,"vendor_id":"call_646941"}]},"response_id":"ZcvQasXYKqGxqtsPpbHH0Ac","run_id":"20261015T124724Z-suite-nist_univariate-gemini-28801c","seq":3,"task_id":"nist_univariate.pidigits.agent","timestamp":"2026-10-15T12:47:33.194611Z","usage":{"cached":16413,"input":18927,"output":94,"thoughts":2240},"vendor":true}
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{"answer":"For the 5000 observations of the PiDigits dataset in column y, the sample statistics are:\n\n- Sample mean: 4.5348\n- Sample standard deviation (denominator n \u2212 1): 2.86734","answer_figure":{"call_id":"call_743b03b3","ci_high":null,"ci_low":null,"formatted":5000.0,"key":"n_rows","ref":"call_743b03b3:n_rows","se":null,"tool_id":"data.describe","unit":"rows","value":5000.0},"attempt":1,"caveats":[],"epoch":1,"event":"answer","figure_refs":["call_743b03b3:n_rows"],"run_id":"20261015T124724Z-suite-nist_univariate-gemini-28801c","seq":5,"stopped":"answered","task_id":"nist_univariate.pidigits.agent","timestamp":"2026-10-15T12:47:54.134162Z"}
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{"attempt":1,"check":{"check_index":4,"detail":"columns[{'column': 'y'}].sd = 2.86733906 vs 2.86733906028871","expected":2.86733906028871,"failure_class":null,"kind":"table_close","observed":2.867339060288708,"passed":true,"required":true,"source_id":"nist_univariate.pidigits.sd","tolerance":{"abs_tol":null,"interval":null,"min_lre":null,"open_interval":false,"rel_tol":1e-09,"rule":null},"trace_call_id":"call_743b03b3","weight":1.0},"epoch":1,"event":"score","run_id":"20261015T124724Z-suite-nist_univariate-gemini-28801c","seq":10,"task_id":"nist_univariate.pidigits.agent","timestamp":"2026-10-15T12:47:54.134162Z"}