{"call_id":"call_d295baf9","run_id":"20261015T170050Z-suite-crb_papers-scripted-8b13b2/crb_papers.q085/e1a1","step":"data","kind":"note","name":"data.transform","version":"","code_version":"62cdbe0860ad","inputs":{"dataset":"ref_rd","index":0,"op":"select","args":{"columns":["outcome_var","votemargin"]}},"input_digest":"","dataset_digests":{},"result":null,"outputs":{"rows_before":1504,"rows_after":1504,"frame_sha256_before":"576276515be0a21f031e0dfac5aca20e6617545eb6e8dd56d2d7de10d144f309","frame_sha256_after":"9db19a4680161ee84d0fef905ffac6e04d5f6c4708171e9e636cd93fddb47da6"},"output_digest":"","params_versions":[],"sources":[],"started_at":"2026-10-15T17:00:55.529778Z","duration_ms":3.294417168945074,"error":null,"decided_by":"code","rationale":"","cached":false,"informed_by":[],"model":null,"prompt":null,"response":null,"tokens_in":null,"tokens_out":null,"meta":{}}
{"call_id":"call_4c98b74e","run_id":"20261015T170050Z-suite-crb_papers-scripted-8b13b2/crb_papers.q085/e1a1","step":"data","kind":"note","name":"data.transform","version":"","code_version":"62cdbe0860ad","inputs":{"dataset":"ref_rd","index":1,"op":"dropna","args":{"columns":["outcome_var","votemargin"]}},"input_digest":"","dataset_digests":{},"result":null,"outputs":{"rows_before":1504,"rows_after":1504,"frame_sha256_before":"9db19a4680161ee84d0fef905ffac6e04d5f6c4708171e9e636cd93fddb47da6","frame_sha256_after":"9db19a4680161ee84d0fef905ffac6e04d5f6c4708171e9e636cd93fddb47da6"},"output_digest":"","params_versions":[],"sources":[],"started_at":"2026-10-15T17:00:55.546565Z","duration_ms":0.2982080914080143,"error":null,"decided_by":"code","rationale":"","cached":false,"informed_by":[],"model":null,"prompt":null,"response":null,"tokens_in":null,"tokens_out":null,"meta":{}}
{"call_id":"call_bf4c4081","run_id":"20261015T170050Z-suite-crb_papers-scripted-8b13b2/crb_papers.q085/e1a1","step":"analysis","kind":"llm","name":"llm.analyst.scripted","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":"2026-10-15T17:00:55.588281Z","duration_ms":0.0,"error":null,"decided_by":"model","rationale":"","cached":false,"informed_by":[],"model":"reference","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\": \"ds_0054\",\n \"rows\": 1504,\n \"description\": \"| Variable Name                 | Description                                                                                                   |\\n|------------------------------|---------------------------------------------------------------------------------------------------------------|\\n| Unnamed: 0                   | Row index from the source file                                                                                |\\n| depfed.id                    | Candidate identifier (year–state–code) for the 2006 federal deputy election                                   |\\n| uf                           | State (UF) abbreviation                                                                                        |\\n| party                        | Candidate’s party abbreviation                                                                                 |\\n| rank                         | Candidate’s intra-coalition rank by personal votes                                                             |\\n| coal                         | Party/coalition label on the ballot in the state                                                               |\\n| coalseats                    | Number of seats won by the coalition in the state                                                              |\\n| name                         | Candidate’s full name                                                                                          |\\n| elected                      | Indicator for whether the candidate took office at the start of the term (1=yes, 0=no)                        |\\n| votes                        | Candidate’s total personal (nominal) votes                                                                     |\\n| votemargin                   | Raw vote difference to the relevant intra-coalition cutoff (votes vs last winner/first loser)                  |\\n| margin.coalvote              | Vote margin expressed as a share of coalition votes                                                            |\\n| margin.nomvote               | Vote margin expressed as a share of nominal votes in the state                                                 |\\n| infl.vote.margin             | “Inflated” vote margin measure adjusting for district magnitude differences                                    |\\n| suplencia.days               | Days served as a substitute (suplente) during the term                                                         |\\n| licenca.days                 | Days on leave (licença) for a seated deputy during the term                                                    |\\n| donations                    | Total registered corporate donations received in 2006 (reais, 2006 prices)                                     |\\n| contracts.2004               | Direct federal payments to the candidate’s donor firms in 2004 (reais, 2006 prices)                            |\\n| contracts.2005               | Direct federal payments to the candidate’s donor firms in 2005 (reais, 2006 prices)                            |\\n| contracts.2006               | Direct federal payments to the candidate’s donor firms in 2006 (reais, 2006 prices)                            |\\n| contracts.2007               | Direct federal payments to the candidate’s donor firms in 2007 (reais, 2006 prices)                            |\\n| contracts.2008               | Direct federal payments to the candidate’s donor firms in 2008 (reais, 2006 prices)                            |\\n| contracts.2009               | Direct federal payments to the candidate’s donor firms in 2009 (reais, 2006 prices)                            |\\n| contracts.2010               | Direct federal payments to the candidate’s donor firms in 2010 (reais, 2006 prices)                            |\\n| agriculture                  | -                                                                                                              |\\n| num.contracts08              | Number of direct federal contracts to the candidate’s donor firms in 2008                                      |\\n| num.contracts09              | Number of direct federal contracts to the candidate’s donor firms in 2009                                      |\\n| num.contracts10              | Number of direct federal contracts to the candidate’s donor firms in 2010                                      |\\n| pw.donations                 | Total donations from public-works sector firms (construction, water/sewage, energy) in 2006 (reais)            |\\n| pw.contracts.2004            | Direct federal payments to public-works donor firms in 2004 (reais, 2006 prices)                               |\\n| pw.contracts.2005            | Direct federal payments to public-works donor firms in 2005 (reais, 2006 prices)                               |\\n| pw.contracts.2006            | Direct federal payments to public-works donor firms in 2006 (reais, 2006 prices)                               |\\n| pw.contracts.2007            | Direct federal payments to public-works donor firms in 2007 (reais, 2006 prices)                               |\\n| pw.contracts.2008            | Direct federal payments to public-works donor firms in 2008 (reais, 2006 prices)                               |\\n| pw.contracts.2009            | Direct federal payments to public-works donor firms in 2009 (reais, 2006 prices)                               |\\n| pw.contracts.2010            | Direct federal payments to public-works donor firms in 2010 (reais, 2006 prices)                               |\\n| pw.agriculture               | -                                                                                                              |\\n| pw.num.contracts08           | Number of direct federal contracts to public-works donor firms in 2008                                         |\\n| pw.num.contracts09           | Number of direct federal contracts to public-works donor firms in 2009                                         |\\n| pw.num.contracts10           | Number of direct federal contracts to public-works donor firms in 2010                                         |\\n| contracts.0406               | Sum of direct federal payments to donor firms, 2004–2006 (reais, 2006 prices)                                   |\\n| contracts.0810               | Sum of direct federal payments to donor firms, 2008–2010 (reais, 2006 prices)                                   |\\n| pw.con.0810                  | Sum of direct federal payments to public-works donor firms, 2008–2010 (reais, 2006 prices)                      |\\n| pw.con.0406                  | Sum of direct federal payments to public-works donor firms, 2004–2006 (reais, 2006 prices)                      |\\n| donor.firms                  | Number of corporate donor firms contributing to the candidate in 2006                                           |\\n| pw.donor.firms               | Number of donor firms in public-works industries contributing to the candidate in 2006                         |\\n| nondonors.contracts          | -                                                                                                              |\\n| nondonors.pwcontracts        | -                                                                                                              |\\n| donoronly02.contracts0810    | -                                                                                                              |\\n| donoronly02.pw.con.0810      | -                                                                                                              |\\n| voters                       | Number of registered voters in the candidate’s state (2006)                                                     |\\n| contracts.PTmin.0810         | Payments to donor firms, 2008–2010, from ministries headed by PT (reais, 2006 prices)                          |\\n| pwcontracts.PTmin.0810       | Payments to public-works donor firms, 2008–2010, from ministries headed by PT (reais, 2006 prices)             |\\n| contracts.coalmin.0810       | Payments to donor firms, 2008–2010, from ministries headed by coalition partners (reais, 2006 prices)          |\\n| pwcontracts.coalmin.0810     | Payments to public-works donor firms, 2008–2010, from ministries headed by coalition partners (reais, 2006 prices) |\\n| contracts.dnit.0810          | Payments to donor firms from DNIT (National Department of Transportation Infrastructure), 2008–2010 (reais)     |\\n| donations.dnit               | Total registered donations from firms that received DNIT payments (reais, 2006 prices)                          |\\n| pwcontracts.dnit.0810        | Payments to public-works donor firms from DNIT, 2008–2010 (reais, 2006 prices)                                  |\\n| pw.donations.dnit            | Total registered donations from public-works firms that received DNIT payments (reais, 2006 prices)             |\\n| donorfirms.dnit              | Number of donor firms that received DNIT payments, 2008–2010                                                   |\\n| pw.donorfirms.dnit           | Number of public-works donor firms that received DNIT payments, 2008–2010                                      |\\n| uf_coal                      | Combined state–coalition identifier                                                                             |\\n| rank_discont                 | Distance between candidate’s rank and the coalition’s seat cutoff (negative=below cutoff)                       |\\n| outcome_var                  | Log of the average 2008–2010 direct federal payments per donor firm (reais, +1)                                 |\\n\\nData Description: \\nThis file contains candidate-level data for Brazil’s 2006 Chamber of Deputies election linked to firm-level government payment records. Candidate and donation information were taken from the Superior Electoral Tribunal (TSE), including party/coalition labels, personal votes, intra-coalition rank, and officially registered corporate donations. Donor firms were matched to the Ministry of Finance registry (CNPJ and CNAE) to classify industries, with a focus on public-works activities (construction, water/sewage, and energy).\\n\\nDirect federal payments to firms were extracted from the Office of the Comptroller General’s Transparência portal and aggregated to the candidate level by donor list for 2004–2010 (values converted to 2006 reais). The dataset summarizes totals and counts for all donor firms and for the public-works subset, provides windows for 2004–2006 and 2008–2010, and includes tags for payments executed by DNIT and by ministries led by PT or coalition partners. Additional fields capture electorate size by state and service days as substitute or on leave for seated deputies. This structure allows analysts to study patterns of campaign financing and federal contracting around the 2006 election.\",\n \"columns\": {\n  \"Unnamed: 0\": \"int64\",\n  \"depfed.id\": \"str\",\n  \"uf\": \"str\",\n  \"party\": \"str\",\n  \"rank\": \"int64\",\n  \"coal\": \"str\",\n  \"coalseats\": \"int64\",\n  \"name\": \"str\",\n  \"elected\": \"int64\",\n  \"votes\": \"int64\",\n  \"votemargin\": \"int64\",\n  \"margin.coalvote\": \"float64\",\n  \"margin.nomvote\": \"float64\",\n  \"infl.vote.margin\": \"float64\",\n  \"suplencia.days\": \"int64\",\n  \"licenca.days\": \"int64\",\n  \"donations\": \"float64\",\n  \"contracts.2004\": \"float64\",\n  \"contracts.2005\": \"float64\",\n  \"contracts.2006\": \"float64\",\n  \"contracts.2007\": \"float64\",\n  \"contracts.2008\": \"float64\",\n  \"contracts.2009\": \"float64\",\n  \"contracts.2010\": \"float64\",\n  \"agriculture\": \"int64\",\n  \"num.contracts08\": \"int64\",\n  \"num.contracts09\": \"int64\",\n  \"num.contracts10\": \"int64\",\n  \"pw.donations\": \"float64\",\n  \"pw.contracts.2004\": \"float64\",\n  \"pw.contracts.2005\": \"float64\",\n  \"pw.contracts.2006\": \"float64\",\n  \"pw.contracts.2007\": \"float64\",\n  \"pw.contracts.2008\": \"float64\",\n  \"pw.contracts.2009\": \"float64\",\n  \"pw.contracts.2010\": \"float64\",\n  \"pw.agriculture\": \"int64\",\n  \"pw.num.contracts08\": \"int64\",\n  \"pw.num.contracts09\": \"int64\",\n  \"pw.num.contracts10\": \"int64\",\n  \"contracts.0406\": \"float64\",\n  \"contracts.0810\": \"float64\",\n  \"pw.con.0810\": \"float64\",\n  \"pw.con.0406\": \"float64\",\n  \"donor.firms\": \"int64\",\n  \"pw.donor.firms\": \"int64\",\n  \"nondonors.contracts\": \"float64\",\n  \"nondonors.pwcontracts\": \"float64\",\n  \"donoronly02.contracts0810\": \"float64\",\n  \"donoronly02.pw.con.0810\": \"float64\",\n  \"voters\": \"int64\",\n  \"contracts.PTmin.0810\": \"float64\",\n  \"pwcontracts.PTmin.0810\": \"float64\",\n  \"contracts.coalmin.0810\": \"float64\",\n  \"pwcontracts.coalmin.0810\": \"float64\",\n  \"contracts.dnit.0810\": \"float64\",\n  \"donations.dnit\": \"float64\",\n  \"pwcontracts.dnit.0810\": \"float64\",\n  \"pw.donations.dnit\": \"float64\",\n  \"donorfirms.dnit\": \"int64\",\n  \"pw.donorfirms.dnit\": \"int64\",\n  \"uf_coal\": \"str\",\n  \"rank_discont\": \"int64\",\n  \"outcome_var\": \"float64\"\n }\n}\n{\n \"id\": \"ref_rd\",\n \"rows\": 1504,\n \"description\": \"Complete rows of outcome_var and votemargin\",\n \"columns\": {\n  \"outcome_var\": \"float64\",\n  \"votemargin\": \"int64\"\n }\n}\n\n# Question\nAmong federal deputy candidates in very close races, what is the effect of being elected versus not elected on the subsequent log average value of direct federal government contracts received by their corporate donor firms during 2008-2010?","response":"rdrobust(y, x, c=0.0, h=25000, p=1): the conventional local-linear estimate (coef row 1)\ninfer__rdd({\"dataset\": \"ref_rd\", \"y\": \"outcome_var\", \"running\": \"votemargin\", \"cutoff\": 0.0, \"bandwidth\": 25000.0, \"kernel\": \"triangular\", \"polynomial\": 1})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_bf4c4081","text":"rdrobust(y, x, c=0.0, h=25000, p=1): the conventional local-linear estimate (coef row 1)","tool_calls":[{"vendor_id":"script_1","llm_name":"infer__rdd","tool_id":"infer.rdd","args":{"dataset":"ref_rd","y":"outcome_var","running":"votemargin","cutoff":0.0,"bandwidth":25000.0,"kernel":"triangular","polynomial":1}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"6efbd863832037bbc09f4baa2d76494471b83c3a0e7db07845c33ace4a44bdd9","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["14882f4f0c8000c1a5d4b6e7907e148013248e18ad9c85910ec3a6a79ad21a55"],"config":{}},"attempts":[],"response_id":null,"finish_reason":"tool_use","model_reported":"reference","usage":{"input":0,"output":0,"thoughts":0,"cached":0},"thoughts":null,"vendor":false,"wall_ms":1.0194170754402876}}
{"call_id":"call_cb2476d1","run_id":"20261015T170050Z-suite-crb_papers-scripted-8b13b2/crb_papers.q085/e1a1","step":"analysis","kind":"tool","name":"infer.rdd","version":"0.1.0","code_version":"62cdbe0860ad","inputs":{"dataset":"ref_rd","y":"outcome_var","running":"votemargin","cutoff":0.0,"bandwidth":25000.0,"kernel":"triangular","polynomial":1,"cluster":null,"subset":null,"level":0.95},"input_digest":"20d8e87f8991ecc1e30828a2494be78bfcea75fdefaa8d7a39a0e2be7ad36881","dataset_digests":{"ref_rd":"9db19a4680161ee84d0fef905ffac6e04d5f6c4708171e9e636cd93fddb47da6"},"result":{"tool":"infer.rdd","version":"0.1.0","call_id":"call_cb2476d1","status":"ok","reason":"","figures":[{"key":"effect","value":2.193507668036064,"unit":"","label":"Jump in outcome_var at the cutoff of votemargin","se":1.2219220047382846,"ci_low":-0.20141545316795506,"ci_high":4.588430789240084,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Ajuste linear ponderado (kernel triangular) de cada lado do limiar, com janela h (informada); o efeito é a diferença entre os dois ajustes no limiar.","formula":"","terms":[{"symbol":"h","value":25000.0,"unit":"","note":"janela (informada)"},{"symbol":"n_esq","value":276.0,"unit":"observações abaixo do limiar","note":""},{"symbol":"n_dir","value":187.0,"unit":"observações acima do limiar","note":""}],"result":2.193507668036064,"unit":""},{"text_pt":"Erro-padrão robusto à heterocedasticidade (HC1): a raiz da variância estimada do coeficiente.","formula":"ep = √V_jj","terms":[{"symbol":"V_jj","value":1.4930933856636284,"unit":"","note":"diagonal da matriz de covariância"}],"result":1.2219220047382846,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição normal vezes o erro-padrão.","formula":"IC = τ ± c · ep","terms":[{"symbol":"τ","value":2.193507668036064,"unit":"","note":""},{"symbol":"c","value":1.959963984540054,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":1.2219220047382846,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"bandwidth","value":25000.0,"flag":false,"note":"half-width of the window around the cutoff (informada)"},{"name":"bandwidth_method","value":"user","flag":false,"note":""},{"name":"n_left","value":276,"flag":false,"note":"observations with positive weight below the cutoff"},{"name":"n_right","value":187,"flag":false,"note":"observations with positive weight at or above the cutoff"},{"name":"kernel","value":"triangular","flag":false,"note":""},{"name":"polynomial","value":1,"flag":false,"note":""},{"name":"bandwidth_sensitive","value":false,"flag":false,"note":"the estimates at half and double the bandwidth fall inside the main interval"},{"name":"n_obs","value":1504,"flag":false,"note":"rows used to choose the bandwidth"},{"name":"se_type","value":"HC1","flag":false,"note":"heteroskedasticity-robust standard errors (HC1)"}],"tables":{"sensitivity":[{"multiplier":0.5,"bandwidth":12500.0,"estimate":1.4064672153941093,"se":1.6705163875747624,"ci_low":-1.8676847398363794,"ci_high":4.680619170624598,"n_left":158,"n_right":110},{"multiplier":1.0,"bandwidth":25000.0,"estimate":2.193507668036064,"se":1.2219220047382846,"ci_low":-0.20141545316795506,"ci_high":4.588430789240084,"n_left":276,"n_right":187},{"multiplier":2.0,"bandwidth":50000.0,"estimate":1.6691933392761058,"se":0.8959566003322634,"ci_low":-0.0868493290860779,"ci_high":3.4252360076382895,"n_left":574,"n_right":282}]},"datasets":{},"warnings":[],"assumptions":["units cannot precisely control the running variable around the cutoff","every other determinant of the outcome is continuous at the cutoff"],"notes":""},"outputs":null,"output_digest":"15852ee6b13f60adca7efba0c8de9cc1982a2bf096b48c57e9ac4c014c407913","params_versions":[],"sources":[],"started_at":"2026-10-15T17:00:55.591669Z","duration_ms":6.709166103973985,"error":null,"decided_by":"model","rationale":"rdrobust(y, x, c=0.0, h=25000, p=1): the conventional local-linear estimate (coef row 1)","cached":false,"informed_by":[],"model":null,"prompt":null,"response":null,"tokens_in":null,"tokens_out":null,"meta":{"turn_id":"call_bf4c4081","llm_name":"infer__rdd","vendor_id":"script_1","result_for_model_ref":"163c5106ae0fe5c0d15d9d5acce291cdec21b0399d7a7d72526e700344430682"}}
{"call_id":"call_07b0d4ba","run_id":"20261015T170050Z-suite-crb_papers-scripted-8b13b2/crb_papers.q085/e1a1","step":"analysis","kind":"llm","name":"llm.analyst.scripted","version":"","code_version":"62cdbe0860ad","inputs":{},"input_digest":"","dataset_digests":{},"result":null,"outputs":null,"output_digest":"","params_versions":[],"sources":[],"started_at":"2026-10-15T17:00:55.598552Z","duration_ms":0.0,"error":null,"decided_by":"model","rationale":"","cached":false,"informed_by":[],"model":"reference","prompt":null,"response":"submit_answer({\"answer\": \"Estimativa (infer.rdd): effect = 2,194. Reference path transcribed from the item's own estimation script.\", \"figure_refs\": [\"call_cb2476d1:effect\"], \"caveats\": []})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_07b0d4ba","text":"","tool_calls":[{"vendor_id":"script_2","llm_name":"submit_answer","tool_id":null,"args":{"answer":"Estimativa (infer.rdd): effect = 2,194. Reference path transcribed from the item's own estimation script.","figure_refs":["call_cb2476d1:effect"],"caveats":[]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"6efbd863832037bbc09f4baa2d76494471b83c3a0e7db07845c33ace4a44bdd9","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["14882f4f0c8000c1a5d4b6e7907e148013248e18ad9c85910ec3a6a79ad21a55","dd19c8a79cb0b02ebae09a44483f9f058f4f0183c1972c751373513dc85bcb5c","ac32bc8018b67eba6fa26847bbfb63a7966ddd561267eb66a74f95637409bd5c"],"config":{}},"attempts":[],"response_id":null,"finish_reason":"tool_use","model_reported":"reference","usage":{"input":0,"output":0,"thoughts":0,"cached":0},"thoughts":null,"vendor":false,"wall_ms":0.2882909029722214}}
{"call_id":"call_76ba61ad","run_id":"20261015T170050Z-suite-crb_papers-scripted-8b13b2/crb_papers.q085/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":"2026-10-15T17:00:55.600136Z","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_07b0d4ba","numbers_found":["2,194"],"unbacked":[],"uncited":[],"invalid_refs":[],"empty":false,"verdict":"accepted","retries_left":1,"vendor_id":"script_2"}}