Tarefa paper_repro.ccft_2017.senate_h16794.agent

Veredito

Pergunta feita ao analista

Calonico, Cattaneo, Farrell and Titiunik (2017), 'rdrobust: Software for Regression-discontinuity Designs', the U.S. Senate example (data of Cattaneo, Frandsen and Titiunik 2015): estimate the incumbency advantage as a sharp regression discontinuity, the jump at a Democratic lead of zero in the Democratic party's share of the votes in the next election for the same seat, where the running variable is the Democratic lead over the strongest opponent in the current election, in percentage points. Use a local linear regression on each side of zero with kernel weights that fall linearly from one at zero to nothing at the bandwidth, and the bandwidth h = 16.79369 percentage points that Calonico, Cattaneo and Titiunik (2014) selected for this example (use it as given; do not recompute it). Report the conventional point estimate of the jump.

Valores esperados e de onde vêm

Verificações

Caminho de referência e caminho do agente

A chamada de referência ao lado da chamada que deu a resposta, argumento por argumento.

Trajetória

Cada evento na ordem em que aconteceu: turnos do modelo, chamadas de ferramentas, guardas, transformações e pontuação.

1. 0Início

   Calonico, Cattaneo, Farrell and Titiunik (2017), 'rdrobust: Software for Regression-discontinuity Designs', the U.S. Senate example (data of Cattaneo, Frandsen and Titiunik 2015): estimate the incumbency advantage as a sharp regression discontinuity, the jump at a Democratic lead of zero in the Democratic party's share of the votes in the next election for the same seat, where the running variable is the Democratic lead over the strongest opponent in the current election, in percentage points. Use a local linear regression on each side of zero with kernel weights that fall linearly from one at zero to nothing at the bandwidth, and the bandwidth h = 16.79369 percentage points that Calonico, Cattaneo and Titiunik (2014) selected for this example (use it as given; do not recompute it). Report the conventional point estimate of the jump.

   senate (agent)

   U.S. Senate elections, 1914-2010, the extract of the data of Cattaneo, Frandsen and Titiunik (2015) that ships with the rdrobust package as its illustration: one row per state and Senate election (1,390 rows). Columns: state, state code; year, year of the election; margin, the Democratic party's margin of victory in that election, in percentage points (negative when the Democratic candidate lost); vote, the Democratic party's vote share, in percent, in the next election for the same Senate seat (missing for some rows); class, the seat's Senate class (1, 2 or 3); termshouse and termssenate, terms the Democratic candidate had served in the House and in the Senate; population, the state's population.

   file def1fc6b8ae5af273f2140aa449c81b3ecfe1d0f325aa80ceccb0d9638a2ad44 · frame —
2. 1Turno do modelo
   call_38e06eafgemini-3.8-flashFim: STOP8.208 ms14444 de entrada · 37 de saída · 374 de raciocínio · 0 em cache

   Texto visível

   I will load the regression discontinuity skill to review best practices and required checks for this design.

   Resumo do raciocínio, como declarado pelo modelo

   **My Thought Process for Estimating Incumbency Advantage in US Senate Elections via RDD** My objective here is to precisely quantify the incumbency advantage in US Senate elections. To achieve this, I'm going to employ a sharp regression discontinuity design (RDD). The dataset I'll be working with is the `senate` dataset. The core of my RDD setup involves defining the outcome variable and the running variable. My outcome variable, which is what I'm trying to explain or predict, will be `vote`. Specifically, this represents the Democratic party's vote share, expressed as a percentage, in the election immediately following the one where the `margin` was observed for the same Senate seat. The running variable is `margin`, which is the Democratic margin of victory in percentage points from the *previous* election. The crucial aspect of an RDD is the cutoff point, and in this scenario, the cutoff is set at 0. This means I'm comparing elections where the Democratic candidate won by a very slim margin (just above 0) to those where they narrowly lost (just below 0). The assumption is that these elections are otherwise very similar, and any significant jump in the outcome variable at the cutoff is attributable to the incumbency advantage. For the estimation itself, I'm going to use a local linear regression. This means I'll fit a separate linear model on each side of the cutoff (i.e., for margins greater than 0 and for margins less than 0). The polynomial order will be 1, which aligns with the local linear approach. To give more weight to observations closer to the cutoff and less weight to those further away, I'll employ a triangular kernel. This kernel smoothly downweights observations as they move away from the cutoff, reaching zero weight at the specified bandwidth. So, I'll set `kernel="triangular"`. The bandwidth selection is critical for RDD. I'll be using the bandwidth $h = 16.79369$ percentage points. This value has been chosen using the methodology proposed by Calonico, Cattaneo, and Titiunik (2014), so I will directly pass this value using the `bandwidth` parameter. The cutoff, as I've established, is 0, so `cutoff=0`. My primary goal is to report the conventional point estimate of the jump in the `vote` variable at the `margin` cutoff of 0. This jump will directly represent my estimate of the incumbency advantage. When I go to execute this, I'll be looking at the `infer__rdd` function. I'll need to specify the following parameters: * `dataset`: "senate" * `y`: "vote" * `running`: "margin" * `cutoff`: 0 * `bandwidth`: 16.79369 * `kernel`: "triangular" * `polynomial`: 1 Now, a key consideration in RDD analysis is to ensure the validity of the design. The prompt mentions best practices, and this brings to mind the importance of density tests, specifically the McCrary test, which helps determine if there's a discontinuity in the density of the running variable at the cutoff. This is a crucial diagnostic to ensure that treatment assignment (winning or losing by a slim margin) is not being manipulated. The prompt itself suggests: "Before estimating, work out the design... Read the diagnostics, warnings and 'Check after' items a result returns before you trust a figure... Often followed by: infer.density_test". This reinforces my intuition. So, before I solely rely on the RDD estimate, I should ideally also run a density test, possibly using `infer.density_test`. I'll first need to ensure the `regression-discontinuity` skill is loaded or directly call the relevant inference functions. My immediate next step is to proceed with the `infer__rdd` call with the specified parameters, but I'll keep the density test in mind as a crucial follow-up step to validate the RDD's assumptions.

   Chamadas pedidas

   • load_skill (meta-ferramenta)

     {
       "name": "regression-discontinuity"
     }

   Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
3. 2Habilidade
   regression-discontinuityTexto da habilidade
4. 3Turno do modelo
   call_caeacc1agemini-3.8-flashFim: STOP4.382 ms15348 de entrada · 87 de saída · 28 de raciocínio · 0 em cache

   Texto visível

   I will estimate the sharp regression discontinuity using a local linear regression with a triangular kernel and the specified bandwidth of 16.79369.

   Resumo do raciocínio, como declarado pelo modelo

   **My Thought Process on Code Verification and Testing** Alright, so the immediate plan is to execute `infer__rdd` with the precise parameters we've defined. This is the core operational step I'm focused on. But it's not just about running it; I need to be sure about the integrity of the output, specifically regarding sorting. To achieve this, I'll also be invoking `infer__density_test`. This second call serves as a critical verification mechanism, a sort of sanity check to confirm that the sorting, which is fundamental to the expected behavior of `infer__rdd`, is indeed correct. Essentially, it's a two-pronged approach: execute the primary function and then immediately validate a key characteristic of its output using a dedicated testing utility.

   Chamadas pedidas

   • infer.rdd

     {
       "bandwidth": 16.79369,
       "cutoff": 0,
       "dataset": "senate",
       "kernel": "triangular",
       "polynomial": 1,
       "running": "margin",
       "y": "vote"
     }

   Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
5. 4Ferramenta
   infer.rddv0.1.0call_fc1c1b10decidido pelo modeloreexecutável

   Motivo: I will estimate the sharp regression discontinuity using a local linear regression with a triangular kernel and the specified bandwidth of 16.79369.

   Argumentos

   {
     "bandwidth": 16.79369,
     "cluster": null,
     "cutoff": 0,
     "dataset": "senate",
     "kernel": "triangular",
     "level": 0.95,
     "polynomial": 1,
     "running": "margin",
     "subset": null,
     "y": "vote"
   }

   Figuras call_fc1c1b10

   Chave	Valor	Valor bruto	Erro-padrão	IC
   effect Jump in vote at the cutoff of margin	7,4253	7.425304341122355	1,49791.497901791714546	4,48947 – 10,3611

   Diagnósticos

   • bandwidth = 16,793716.79369
   • bandwidth_method = "user"
   • n_left = 343343
   • n_right = 310310
   • kernel = "triangular"
   • polynomial = 11
   • bandwidth_sensitive = false
   • n_obs = 1.2971297
   • se_type = "HC1"

   Tabelas (1)

   {
     "sensitivity": [
       {
         "bandwidth": 8.396845,
         "ci_high": 12.665187371055993,
         "ci_low": 4.845539163400076,
         "estimate": 8.755363267228034,
         "multiplier": 0.5,
         "n_left": 217,
         "n_right": 175,
         "se": 1.9948448719814
       },
       {
         "bandwidth": 16.79369,
         "ci_high": 10.361137905260883,
         "ci_low": 4.489470776983827,
         "estimate": 7.425304341122355,
         "multiplier": 1,
         "n_left": 343,
         "n_right": 310,
         "se": 1.497901791714546
       },
       {
         "bandwidth": 33.58738,
         "ci_high": 9.455366988625087,
         "ci_low": 5.051427501358595,
         "estimate": 7.253397244991841,
         "multiplier": 2,
         "n_left": 499,
         "n_right": 488,
         "se": 1.1234745949426124
       }
     ]
   }

   • 93 rows were dropped because one of 'vote', 'margin' was missing

   Digests dos dados

   senate e1181ed0f741b1485e582b1110d74d93e57241b2b9bfa1ed59f2cd80ab0b165e

   sha256 do resultado

   f1ad2da0e331d60b6f43b4d553cbc1fe3d8fdf793e4e8f5399e8837877540602

   Resultado mostrado ao modelo
6. 5Turno do modelo
   call_afa47e1cgemini-3.8-flashFim: STOP4.622 ms15839 de entrada · 54 de saída · 33 de raciocínio · 12626 em cache

   Texto visível

   I will run the McCrary density test on the running variable around the cutoff to check for sorting or manipulation of election margins.

   Resumo do raciocínio, como declarado pelo modelo

   **My Thought Process on Testing for Bunching at a Cutoff** Right, so my immediate objective here is to rigorously examine the `margin` variable for any signs of unnatural clustering or "bunching" precisely at a cutoff point of zero. I'm using the `infer__density_test` function for this specific purpose. This test is crucial because the presence of significant bunching at a cutoff can indicate potential issues with data manipulation, measurement error, or even the underlying treatment assignment mechanism itself. If I find that observations are disproportionately concentrated exactly at the cutoff, it strongly suggests that something is influencing their placement there, which could compromise the validity of any causal inference I intend to draw from this data. Therefore, by setting the cutoff to zero and employing this density test, I'm aiming to identify and quantify any such non-random sorting that might be occurring.

   Chamadas pedidas

   • infer.density_test

     {
       "cutoff": 0,
       "dataset": "senate",
       "running": "margin"
     }

   Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
7. 6Ferramenta
   infer.density_testv0.1.0call_91bbb70cdecidido pelo modeloreexecutável

   Motivo: I will run the McCrary density test on the running variable around the cutoff to check for sorting or manipulation of election margins.

   Argumentos

   {
     "bandwidth": null,
     "bin_size": null,
     "cutoff": 0,
     "dataset": "senate",
     "running": "margin"
   }

   Figuras call_91bbb70c

   Chave	Valor	Valor bruto	Erro-padrão	IC
   theta Log difference of the density of margin just above and just below the cutoff	-0,100746 log points	-0.10074561257565895	0,1171450.11714504761244064	—
   se Standard error of theta	0,117145 log points	0.11714504761244064	—	—
   z z statistic of theta	-0,860007	-0.8600074405958917	—	—
   p_value p-value of no density jump at the cutoff	0,389785 probability	0.3897849409887445	—	—

   Diagnósticos

   • method = "mccrary"
   • manipulation = false
   • bin_size = 1,841331.8413302112363994
   • bandwidth = 25,849425.849380231330052
   • n_obs = 1.3901390
   • f_left = 0,02050150.020501508807541136
   • f_right = 0,01853670.01853670594028512
   • n_distinct = 1.3521352

   Digests dos dados

   senate e1181ed0f741b1485e582b1110d74d93e57241b2b9bfa1ed59f2cd80ab0b165e

   sha256 do resultado

   fcaf33e04da28b69d53375075c85b233abd57028ff42e506e67f78c463c5a833

   Resultado mostrado ao modelo
8. 7Turno do modelo
   call_59212549gemini-3.8-flashFim: STOP19.782 ms16209 de entrada · 229 de saída · 1384 de raciocínio · 12590 em cache

   Resumo do raciocínio, como declarado pelo modelo

   **My Thought Process: Estimating Incumbency Advantage in the US Senate** Alright, let's break down this request. The core task is to estimate the incumbency advantage in US Senate elections using a sharp regression discontinuity design, based on the `rdrobust` package and a specific dataset. I need to act as if I'm thinking through the problem myself, as an expert in the field. First, I'll carefully read and understand the prompt's instructions. It specifies using the U.S. Senate example from Cattaneo, Frandsen, and Titiunik (2015), with the data provided by them. The goal is to estimate the "jump" – the incumbency advantage – at the point where a Democratic candidate has zero lead (or is slightly behind) in the current election's vote share. The running variable is precisely this "Democratic lead over the strongest opponent in the current election, in percentage points." The method is a sharp RD, employing local linear regression on either side of the cutoff (zero lead), with a triangular kernel and a pre-specified bandwidth of $h = 16.79369$ percentage points. Crucially, I am *not* to recompute this bandwidth; it's given. The final output requires the "conventional point estimate of the jump." Now, I need to consult Rule 7, which dictates the format of the final answer. It states the answer should be in the *language of the question* (which is English, so I'll write in English), short and direct. It needs to include: 1. The estimate with its uncertainty. 2. The design that identifies it. 3. What could undermine it. It also specifies how to handle figure references (`figure_refs`) and diagnostics that limit conclusions (`caveats`). Numbers must be reported exactly as the tools stated them, and I shouldn't use rule-of-thumb thresholds as numbers. So, my thought process involves: 1. **Identifying the core analysis:** I'm looking at a sharp regression discontinuity design. The outcome variable is the vote share in the *next* election for the same seat, and the running variable is the Democratic lead in the *current* election. The discontinuity is at a Democratic lead of zero. 2. **Confirming the method:** Local linear regression, triangular kernel, given bandwidth ($h = 16.79369$ pp). 3. **Extracting the key result:** I need the conventional point estimate of the jump. This will come from the `rdrobust` output. 4. **Gathering supporting details:** * The standard error of the estimate. * The confidence interval. * The specific design elements (RD, local linear, bandwidth). * Potential threats to identification – this is where tests like the McCrary test for manipulation of the running variable become critical. 5. **Formatting the output according to Rule 7:** * **Language:** English. * **Conciseness:** Short and direct. * **Content:** Estimate, uncertainty, design, threats. * **`figure_refs`:** List any figures quoted, e.g., `call_id:key`. I'll need to check the tool outputs for relevant call IDs. * **`caveats`:** Note any limitations or data issues, like missing values that led to observations being dropped. I'll look for diagnostics related to data usage. * **Numbers:** Exactly as stated by the tools. Let's look at the "Numbers" section provided in the prompt, which seems to be the output from the analysis. This is crucial for the final summary: * **Conventional point estimate of the jump:** 7.4253 (percentage points). This is my primary effect estimate. * **Standard error:** 1.4979. This quantifies the uncertainty around my point estimate. * **95% confidence interval:** [4.48947, 10.3611]. This provides a range for the true effect. * **`call_id` for the effect:** `call_fc1c1b10:effect`. This will go into `figure_refs`. * **Bandwidth:** 16.79369. This is the specified window around the cutoff. * **Observations inside bandwidth:** 343 (`n_left`), 310 (`n_right`). This shows the data density at the cutoff. * **Total observations used:** 1297 (93 dropped due to missing values). This is important for `caveats`. * **McCrary test:** theta = -0.100746, p-value = 0.389785. This tests for manipulation of the running variable. The p-value (0.389785) is high, suggesting no significant manipulation, which is good for identification. The `call_id`s for this are `call_91bbb70c:p_value` and `call_91bbb70c:theta`. Now, I need to synthesize this into the specified format. 1. **Estimate and Uncertainty:** The conventional point estimate of the jump (incumbency advantage) is 7.4253 percentage points, with a standard error of 1.4979 and a 95% confidence interval of [4.48947, 10.3611]. 2. **Design:** This is identified using a sharp regression discontinuity design, specifically a local linear regression with a triangular kernel and a bandwidth of 16.79369 percentage points. The running variable is the Democratic lead in the current election, and the outcome is the vote share in the next election for the same seat. 3. **Undermining Factors (or lack thereof):** A key potential threat to identification is manipulation of the running variable. The McCrary density test does not reveal significant evidence of sorting at the cutoff (p-value = 0.389785), suggesting the assignment of candidates around the zero lead is not manipulated. 4. **`figure_refs`:** I need to include the call ID for the effect estimate and potentially the McCrary test p-value. So, `["call_fc1c1b10:effect", "call_91bbb70c:p_value"]`. 5. **`caveats`:** The prompt mentioned that 93 observations were dropped due to missing values in 'vote' or 'margin'. This is a data caveat. So, `["93 rows were dropped because one of 'vote', 'margin' was missing."]` Putting it all together in the requested format: The conventional point estimate of the jump at the cutoff of zero (the incumbency advantage) is 7.4253 percentage points (standard error 1.4979; 95% CI [4.48947, 10.3611]). This estimate is identified by a sharp regression discontinuity using a local linear regression with a triangular kernel and a bandwidth of 16.79369 percentage points (with 343 observations below and 310 observations at or above the threshold). A McCrary density test shows no evidence of sorting or manipulation at the cutoff (p-value 0.389785). `figure_refs`: `["call_fc1c1b10:effect", "call_91bbb70c:p_value"]` `caveats`: `["93 rows were dropped because one of 'vote', 'margin' was missing."]` This seems to cover all the requirements precisely. The language is English as requested, the numbers are as given, the design and potential threats are mentioned, and the `figure_refs` and `caveats` are included as specified. The summary is short and direct, summarizing my thought process.

   Chamadas pedidas

   • submit_answer (meta-ferramenta)

     {
       "answer": "The conventional point estimate of the jump in the Democratic vote share at the cutoff of zero (the incumbency advantage) is 7.4253 percentage points (standard error 1.4979; 95% CI [4.48947, 10.3611]). \n\nThis effect is identified by a sharp regression discontinuity design using local linear regression with triangular kernel weights and the fixed bandwidth of 16.79369 percentage points, which includes 343 observations below the cutoff and 310 observations at or above it. A McCrary density test reveals no evidence of manipulation or sorting of the vote margin at the threshold (p-value 0.389785).",
       "caveats": [
         "93 rows were dropped because one of 'vote', 'margin' was missing."
       ],
       "figure_refs": [
         "call_fc1c1b10:effect",
         "call_91bbb70c:p_value"
       ]
     }

   Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
9. 8Guarda
   resposta recusada

   Números encontrados

   7.4253 · 1.4979 · 95% · 4.48947 · 10.3611 · 16.79369 · 343 · 310 · 0.389785 · 93

   Sem lastro

   93

   Não citados

   —

   0 nova(s) tentativa(s) restante(s)
10. 9Turno do modelo
    call_a068a17fgemini-3.8-flashFim: STOP6.280 ms17911 de entrada · 223 de saída · 292 de raciocínio · 0 em cache

    Resumo do raciocínio, como declarado pelo modelo

    **My Thought Process on Refining the Data Presentation** Right, so I've just reviewed the flagged issues in the data presentation, and I'm thinking through how to best address them. My primary goal here is to ensure the output is both accurate and as clean as possible, especially for an audience that understands the nuances of this kind of data. First, that `93` in `caveats` was a red flag. The validator specifically pointed out "Numbers no tool call stated: 93." This tells me that any number appearing in the `caveats` section *must* correspond to a recognized tool figure or diagnostic. Since `93` doesn't fit that criteria, it needs to go. The validator also mentioned that `warnings` isn't a checked table, so the responsibility falls on me to ensure the `caveats` are numerically sound in relation to the figures and diagnostics we're presenting. The suggested phrasing, "Observations were dropped because one of 'vote', 'margin' was missing," is a good, direct way to convey the reason without introducing an unsupported number. Then, I'm scanning the list of other numbers to see if they're all accounted for properly. I see: * `7.4253` (effect) * `1.4979` (standard error) * `4.48947` (confidence interval low) * `10.3611` (confidence interval high) * `16.79369` (this one is noted as "in question and diagnostic" - I need to ensure its context is clear and justified) * `343` (diagnostic `n_left`) * `310` (diagnostic `n_right`) * `0.389785` (p-value) My immediate reaction is that I need to be absolutely stringent about not including any numbers in the `caveats` or the final `answer` that aren't explicitly defined as figures or diagnostics. This means if a number isn't directly tied to a calculated metric or a diagnostic count, it's out. The goal is to maintain clarity and avoid any ambiguity that might arise from extraneous numbers. Therefore, my revised approach is to be very disciplined with numerical inclusions. I'll make sure that any numbers I present are either directly a part of the core analysis (like effect sizes, confidence intervals, p-values) or are clearly labeled diagnostic counts. Anything else, particularly in explanatory text like `caveats`, will be removed. I'll re-submit with this stricter filtering in mind.

    Chamadas pedidas

    • submit_answer (meta-ferramenta)

      {
        "answer": "The conventional point estimate of the jump in the Democratic vote share at the cutoff of zero (the incumbency advantage) is 7.4253 percentage points (standard error 1.4979; 95% CI [4.48947, 10.3611]).\n\nThis effect is identified by a sharp regression discontinuity design using local linear regression with triangular kernel weights and the bandwidth of 16.79369 percentage points, including 343 observations below the cutoff and 310 observations at or above it. A McCrary density test indicates no evidence of sorting or manipulation at the threshold (p-value 0.389785).",
        "caveats": [
          "Observations were dropped because one of the variables ('vote' or 'margin') was missing."
        ],
        "figure_refs": [
          "call_fc1c1b10:effect",
          "call_91bbb70c:p_value"
        ]
      }

    Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
11. 10Guarda
    resposta aceita

    Números encontrados

    7.4253 · 1.4979 · 95% · 4.48947 · 10.3611 · 16.79369 · 343 · 310 · 0.389785

    Sem lastro

    —

    Não citados

    —

    0 nova(s) tentativa(s) restante(s)
12. 11Resposta

    The conventional point estimate of the jump in the Democratic vote share at the cutoff of zero (the incumbency advantage) is 7.4253 percentage points (standard error 1.4979; 95% CI [4.48947, 10.3611]). This effect is identified by a sharp regression discontinuity design using local linear regression with triangular kernel weights and the bandwidth of 16.79369 percentage points, including 343 observations below the cutoff and 310 observations at or above it. A McCrary density test indicates no evidence of sorting or manipulation at the threshold (p-value 0.389785). Ressalvas: Observations were dropped because one of the variables ('vote' or 'margin') was missing.

    • Observations were dropped because one of the variables ('vote' or 'margin') was missing.

    Figuras citadas: call_fc1c1b10:effect, call_91bbb70c:p_value · Parada: answered

    Figura da resposta: call_fc1c1b10:effect = 7,42537.425304341122355 (se 1,49791.497901791714546)
13. 12Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
14. 13Pontuação
    #1figura da resposta escolhidapassouobrigatóriaanswer figure call_fc1c1b10:effect (infer.rdd)
15. 14Pontuação
    #2ferramenta chamadapassouobrigatóriaanswer call is infer.rdd
16. 15Pontuação
    #3argumento igualpassouobrigatóriainfer.rdd.y = 'vote'
17. 16Pontuação
    #4argumento igualpassouobrigatóriainfer.rdd.running = 'margin'
18. 17Pontuação
    #5argumento igualpassouobrigatóriainfer.rdd.cutoff = 0.0
19. 18Pontuação
    #6argumento igualpassouobrigatóriainfer.rdd.bandwidth = 16.79369
20. 19Pontuação
    #7argumento igualpassouobrigatóriainfer.rdd.kernel = 'triangular'
21. 20Pontuação
    #8argumento igualpassouobrigatóriainfer.rdd.polynomial = 1
22. 21Pontuação
    #9diagnósticopassouobrigatória343 vs 343
23. 22Pontuação
    #10diagnósticopassouobrigatória310 vs 310
24. 23Pontuação
    #11figura da resposta próximapassouobrigatória7.425304341 vs 7.425304341122386
25. 24Pontuação
    #12figura da resposta próximapassouregistrada1.497901792 vs 1.497901791714553
26. 25Pontuação
    #13figura da resposta próximapassouregistrada7.425304341 vs 7.4253
27. 26Pontuação
    #14figura da resposta próximafalhouregistrada1.497901792 vs 1.4954

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• época 1, tentativa 1passoupontuada(exibindo)

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