Tarefa paper_repro.callaway_santanna_2021.mpdta_event_time.agent

Veredito

Pergunta feita ao analista

Callaway and Sant'Anna (2021), 'Difference-in-Differences with Multiple Time Periods', as in the did package's documented example on its county panel (2003-2007): estimate the group-time average treatment effects of a state minimum-wage increase on log county teen employment, using the never-treated counties as the comparison group, no covariates and the package's default (varying) base period, and aggregate them by time since adoption into the event-study (dynamic) effects, from three periods before adoption to three after, weighting the cohorts by their size. Report the dynamic effects.

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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

   Callaway and Sant'Anna (2021), 'Difference-in-Differences with Multiple Time Periods', as in the did package's documented example on its county panel (2003-2007): estimate the group-time average treatment effects of a state minimum-wage increase on log county teen employment, using the never-treated counties as the comparison group, no covariates and the package's default (varying) base period, and aggregate them by time since adoption into the event-study (dynamic) effects, from three periods before adoption to three after, weighting the cohorts by their size. Report the dynamic effects.

   mpdta (agent)

   The did package's example panel mpdta (Callaway and Sant'Anna 2021): county-level teen employment in 500 U.S. counties, 2003-2007, one row per county and year. Some states first raised their minimum wage in 2004, some in 2006 and some in 2007; the others did not in the window. Columns: countyreal, the county id; year; lemp, the log of county teen employment; lpop, the log of county population; first_treat, the year the county's state first raised its minimum wage (2004, 2006 or 2007; 0 for counties never treated in the window); treat, 1 for counties in states that raised it at some point in the window.

   file 0be3bc5eaaedaa50e047deaf45ad0e193e7210df1644103c63a6c1baa75965d4 · frame —

   1. rename {"map":{"first.treat":"first_treat"}}
2. 1Transformação
   mpdta · rename {"map":{"first.treat":"first_treat"}}

   2500 → 2500 linhas

   Frame antes → depois: 76542064d0d8 → ede9279f5a99
3. 2Turno do modelo
   call_b1183139gemini-3.8-flashFim: STOP4.595 ms14365 de entrada · 46 de saída · 167 de raciocínio · 0 em cache

   Texto visível

   I will load the differences-in-differences skill to confirm best practices and parameter handling for staggered adoption models.

   Resumo do raciocínio, como declarado pelo modelo

   **My Thought Process on Replicating Callaway and Sant'Anna (2021) with the `mpdta` Dataset** My primary objective here is to meticulously replicate or closely estimate the difference-in-differences (DiD) methodology as presented by Callaway and Sant'Anna (2021). I'm particularly interested in applying this to the `mpdta` dataset, which I understand contains the relevant information. Specifically, I need to define the key components of this analysis: * **Outcome Variable:** The dependent variable of interest is `lemp`, which represents the log of county-level teen employment. * **Unit of Analysis:** The observations are at the county level, identified by `countyreal`. * **Time Dimension:** The analysis will be conducted over time, with the `year` variable serving as the time indicator. * **Treatment Assignment:** The timing of the initial treatment for each unit is captured by the `first_treat` variable. * **Comparison Group:** I need to isolate a stable comparison group that has `never_treated`. * **Event Study Design:** The core of this replication involves estimating dynamic event-study effects. This means I want to see how the outcome (`lemp`) changes in relation to the treatment adoption, specifically looking at periods from -3 to +3 years relative to the treatment year. Alternatively, I might consider aggregating these dynamic effects if that proves more insightful or parsimonious. To achieve this, my immediate thought is to explore the available tools for conducting staggered DiD estimations. I'll be looking for a skill that directly addresses this type of analysis, or more specifically, examining the functionality of the `infer__did_staggered` tool. My next step will be to thoroughly understand what `infer__did_staggered` accomplishes and to meticulously review its parameters to ensure I configure it correctly for this specific application, aligning with the precise requirements of the Callaway and Sant'Anna (2021) framework.

   Chamadas pedidas

   • load_skill (meta-ferramenta)

     {
       "name": "differences-in-differences"
     }

   Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
4. 3Habilidade
   differences-in-differencesTexto da habilidade
5. 4Turno do modelo
   call_e49b6f7bgemini-3.8-flashFim: STOP5.365 ms15136 de entrada · 98 de saída · 155 de raciocínio · 0 em cache

   Texto visível

   I will estimate the staggered difference-in-differences model on county teen employment using Callaway and Sant'Anna's estimator with never-treated counties as the control group.

   Resumo do raciocínio, como declarado pelo modelo

   **My Thought Process for Inferring Staggered Adoption Effects** My immediate inclination is to call the `infer__did_staggered` function, and my immediate focus is on understanding its parameters. I need to specify the `dataset` as 'mpdta', the outcome variable `y` as 'lemp', and the unit of observation `unit` as 'countyreal'. The `time` variable is clearly 'year'. The critical element for staggered difference-in-differences is the `first_treat` variable, which I've identified as 'first_treat'. I also need to confirm the `control_group`; while 'never_treated' is the default, I want to be certain it's applied as intended. Finally, I need to decide on the `level` for confidence intervals, settling on 0.95, and I'll also note the default for `n_boot`, likely around 199, though I'll verify this to ensure sufficient bootstrapping. This systematic parameterization is crucial for a robust and accurate inference of staggered adoption effects.

   Chamadas pedidas

   • infer.did_staggered

     {
       "control_group": "never_treated",
       "dataset": "mpdta",
       "first_treat": "first_treat",
       "time": "year",
       "unit": "countyreal",
       "y": "lemp"
     }

   Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
6. 5Ferramenta
   infer.did_staggeredv0.1.0call_a3b16404decidido pelo modeloreexecutável

   Motivo: I will estimate the staggered difference-in-differences model on county teen employment using Callaway and Sant'Anna's estimator with never-treated counties as the control group.

   Argumentos

   {
     "control_group": "never_treated",
     "dataset": "mpdta",
     "first_treat": "first_treat",
     "level": 0.95,
     "n_boot": 199,
     "seed": 0,
     "time": "year",
     "unit": "countyreal",
     "y": "lemp"
   }

   Figuras call_a3b16404

   Chave	Valor	Valor bruto	Erro-padrão	IC
   att Average effect on lemp across post-adoption cohort-periods (Callaway–Sant'Anna)	-0,0399513	-0.03995127515517701	0,01194110.011941084528484509	-0,0633554 – -0,0165472

   Diagnósticos

   • pretrend_mean = -0,000596958-0.0005969576739334104
   • pretrend_se = 0,006825270.006825271151463481
   • n_cohorts = 33
   • n_never_treated = 309309
   • n_units = 500500
   • control_group = "never_treated"
   • n_boot = 199199
   • n_boot_failed = 00

   Tabelas (3)

   {
     "att_gt": [
       {
         "att": -0.010503246220963533,
         "e": 0,
         "g": 2004,
         "n_control": 309,
         "n_g": 20,
         "post": true,
         "se": 0.022882360180335775,
         "t": 2004
       },
       {
         "att": -0.07042315810314909,
         "e": 1,
         "g": 2004,
         "n_control": 309,
         "n_g": 20,
         "post": true,
         "se": 0.035349338097653976,
         "t": 2005
       },
       {
         "att": -0.13725873888940443,
         "e": 2,
         "g": 2004,
         "n_control": 309,
         "n_g": 20,
         "post": true,
         "se": 0.037707820794609354,
         "t": 2006
       },
       {
         "att": -0.10081136308540525,
         "e": 3,
         "g": 2004,
         "n_control": 309,
         "n_g": 20,
         "post": true,
         "se": 0.03498817563354677,
         "t": 2007
       },
       {
         "att": 0.006520112424232907,
         "e": -2,
         "g": 2006,
         "n_control": 309,
         "n_g": 40,
         "post": false,
         "se": 0.02331943920931109,
         "t": 2004
       },
       {
         "att": -0.002750818750518684,
         "e": -1,
         "g": 2006,
         "n_control": 309,
         "n_g": 40,
         "post": false,
         "se": 0.01848490029541098,
         "t": 2005
       },
       {
         "att": -0.00459460695286272,
         "e": 0,
         "g": 2006,
         "n_control": 309,
         "n_g": 40,
         "post": true,
         "se": 0.018221346748964883,
         "t": 2006
       },
       {
         "att": -0.04122447154621793,
         "e": 1,
         "g": 2006,
         "n_control": 309,
         "n_g": 40,
         "post": true,
         "se": 0.019524036066045164,
         "t": 2007
       },
       {
         "att": 0.030506655583292096,
         "e": -3,
         "g": 2007,
         "n_control": 309,
         "n_g": 131,
         "post": false,
         "se": 0.014885220499805493,
         "t": 2004
       },
       {
         "att": -0.0027258928861159572,
         "e": -2,
         "g": 2007,
         "n_control": 309,
         "n_g": 131,
         "post": false,
         "se": 0.01728908780159486,
         "t": 2005
       },
       {
         "att": -0.031087119389688136,
         "e": -1,
         "g": 2007,
         "n_control": 309,
         "n_g": 131,
         "post": false,
         "se": 0.01864182705314265,
         "t": 2006
       },
       {
         "att": -0.026054410719197237,
         "e": 0,
         "g": 2007,
         "n_control": 309,
         "n_g": 131,
         "post": true,
         "se": 0.017105063505205884,
         "t": 2007
       }
     ],
     "by_cohort": [
       {
         "att": -0.07974912657473057,
         "g": 2004,
         "n_g": 20,
         "post_periods": 4
       },
       {
         "att": -0.022909539249540326,
         "g": 2006,
         "n_g": 40,
         "post_periods": 2
       },
       {
         "att": -0.026054410719197237,
         "g": 2007,
         "n_g": 131,
         "post_periods": 1
       }
     ],
     "event_time": [
       {
         "att": 0.030506655583292096,
         "e": -3,
         "n_cohorts": 1,
         "se": 0.014885220499805493
       },
       {
         "att": -0.0005630846263852289,
         "e": -2,
         "n_cohorts": 2,
         "se": 0.013665058460471256
       },
       {
         "att": -0.024458744971168966,
         "e": -1,
         "n_cohorts": 2,
         "se": 0.014563335173162988
       },
       {
         "att": -0.019931816789259777,
         "e": 0,
         "n_cohorts": 3,
         "se": 0.01203765078223572
       },
       {
         "att": -0.050957367065194985,
         "e": 1,
         "n_cohorts": 2,
         "se": 0.016800409715261998
       },
       {
         "att": -0.13725873888940443,
         "e": 2,
         "n_cohorts": 1,
         "se": 0.037707820794609354
       },
       {
         "att": -0.10081136308540525,
         "e": 3,
         "n_cohorts": 1,
         "se": 0.03498817563354677
       }
     ]
   }

   Digests dos dados

   mpdta ede9279f5a99110cfc103b63bc6f1b9052eb1f6f5df8290bc4fd8388ae23fac3

   sha256 do resultado

   1af8c51b4ad61d9ee925c419339e72528e426ba630ebcd8c2a6427f94a788b78

   Resultado mostrado ao modelo
7. 6Turno do modelo
   call_6e7d035dgemini-3.8-flashFim: STOP25.790 ms16435 de entrada · 617 de saída · 1933 de raciocínio · 12610 em cache

   Resumo do raciocínio, como declarado pelo modelo

   **My Thought Process Regarding the Staggered Difference-in-Differences Analysis** Here's a breakdown of my thinking as I processed the output from the `infer.did_staggered` tool, aiming to synthesize the findings into a coherent summary as requested. First, I focused on the core request: to report the dynamic effects of a state minimum-wage increase on log county teen employment, using the Callaway and Sant'Anna (2021) methodology. The prompt specifically asked for these event-study (dynamic) effects from three periods before adoption to three after, weighted by cohort size, and identified using never-treated counties as the comparison group. I immediately turned my attention to the `tables.event_time` output. This table is crucial as it directly provides the aggregated dynamic effects, broken down by time since adoption (e). I meticulously went through each entry, noting the estimated `att` (Average Treatment Effect on the Treated) and its standard error (`se`) for each event time point: * **e = -3:** `att = 0.0305067`, `se = 0.0148852` (based on 1 cohort) * **e = -2:** `att = -0.000563085`, `se = 0.0136651` (based on 2 cohorts) * **e = -1:** `att = -0.0244587`, `se = 0.0145633` (based on 2 cohorts) * **e = 0:** `att = -0.0199318`, `se = 0.0120377` (based on 3 cohorts) * **e = 1:** `att = -0.0509574`, `se = 0.0168004` (based on 2 cohorts) * **e = 2:** `att = -0.137259`, `se = 0.0377078` (based on 1 cohort) * **e = 3:** `att = -0.100811`, `se = 0.0349882` (based on 1 cohort) I recognized that the prompt specifically asked to "Report the dynamic effects," so these values are central to my response. Next, I looked at the `figures` section, specifically `infer.did_staggered`'s `att` key. This represents the overall, cohort-weighted average treatment effect. I noted: * **Overall ATT:** `att = -0.0399513`, `se = 0.0119411`, with a 95% CI of `[-0.0633554, -0.0165472]`. This overall estimate is important as it provides a single summary of the policy's impact across all treated units and time periods, weighted appropriately. Crucially, I also reviewed the `diagnostics` to understand the robustness and limitations of the findings. The prompt implicitly requires me to consider what could undermine the conclusions. * **Pre-trend Check:** The `pretrend_mean` of `-0.00059695767393341` with a `pretrend_se` of `0.00682527115146348` is vital. This indicates that the average effect in the periods *before* treatment adoption was close to zero and statistically indistinguishable from zero. This is a strong piece of evidence supporting the parallel trends assumption, a cornerstone of the difference-in-differences identification strategy. * **Sample Size:** The `n_cohorts` (3) and `n_never_treated` (309) provide context on the data structure and the size of the comparison group. The total `n_units` (500) gives an overall scope. Now, I began to formulate the summary according to the specified rules, particularly Rule 7. **Language and Formatting:** The prompt stated, "Write the answer in the language of the question." Since the question was in English, I would respond in English. I also noted the instruction about decimal commas for Portuguese, but confirmed that standard English decimal points are appropriate here, and the tool's output already uses them, aligning with Rule 1 ("Write numbers as the tools stated them"). **Content Structure:** Rule 7 requires: "the estimate with its uncertainty, the design that identifies it, and what could undermine it." * **Estimate and Uncertainty:** I needed to present the main findings. The prompt specifically asked for "dynamic effects," so listing these from `event_time` was paramount. I also decided to include the overall ATT as a summarizing statistic. * **Design:** The design is clearly stated as Callaway and Sant'Anna (2021) difference-in-differences with multiple time periods, using never-treated counties and a default base period, as per the prompt. I would explicitly mention this. * **What Could Undermine It (Caveats):** This is where the diagnostics and specific observations from the `event_time` table become critical for caveats. * The pre-trend check is a positive finding that *supports* the design, not undermines it, so I'd frame it as a validation of the parallel trends assumption. * However, I noticed that for the later event times (e=2 and e=3), the estimates rely on a single cohort (the earliest adopter, 2004). This is a significant limitation because the longer-term effects are then identified by this specific cohort, not an average across all adopting cohorts. This could mean the longer-term impacts are driven by the initial conditions or characteristics of that first cohort. This is a critical caveat. * I also recalled the standard error calculation method mentioned in the prompt's context (implied by the `did` package example), which is bootstrap-based (199 replications clustered at the county level). While this is a valid method for obtaining standard errors, acknowledging the methodology adds clarity. **Figure References:** Rule 7 also requires `figure_refs` in the format '<call_id>:<key>'. The output provided `call_a3b16404:att` under the `figures` key. This is the primary figure reference. I also considered if any of the `event_time` table entries should be referenced as figures, but the `figures` key specifically points to the overall ATT, not the dynamic effects table itself. **Drafting the `submit_answer`:** Based on the above, I would construct the `submit_answer` as follows: * **`answer`:** This would be a concise summary in English, incorporating the key findings and design. I would present the dynamic effects first, as requested, and then the overall ATT. The language would be direct and professional, suitable for an expert. * **`caveats`:** This list would detail the potential weaknesses or limitations identified, specifically the reliance on a single cohort for longer-term effects. * **`figure_refs`:** This list would contain `call_a3b16404:att`. The specific phrasing for the `answer` and `caveats` would aim to be both informative and adhere to the prompt's implied tone and rigor. For instance, when mentioning the pre-trend, I would state it as evidence for parallel trends, which strengthens the design, rather than a caveat. The caveat would focus on the data limitations for specific time periods. I would also make sure to use the exact numbers provided by the tool. This systematic approach, moving from the core request to data extraction, diagnostic analysis, and finally to formatting and structuring according to the rules, ensures all aspects of the prompt are addressed accurately and efficiently.

   Chamadas pedidas

   • submit_answer (meta-ferramenta)

     {
       "answer": "Using the Callaway and Sant'Anna (2021) estimator with the 309 never-treated counties as the comparison group, the dynamic event-study treatment effects of minimum wage increases on log county teen employment (lemp) from three years before adoption (e = -3) to three years after (e = 3) are:\n\nPre-treatment dynamic effects (placebo/lead tests):\n- e = -3: 0.0305067 (SE: 0.0148852, 1 cohort)\n- e = -2: -0.000563085 (SE: 0.0136651, 2 cohorts)\n- e = -1: -0.0244587 (SE: 0.0145633, 2 cohorts)\n\nPost-treatment dynamic effects:\n- e = 0: -0.0199318 (SE: 0.0120377, 3 cohorts)\n- e = 1: -0.0509574 (SE: 0.0168004, 2 cohorts)\n- e = 2: -0.137259 (SE: 0.0377078, 1 cohort)\n- e = 3: -0.100811 (SE: 0.0349882, 1 cohort)\n\nAcross all post-adoption cohort-periods, the overall cohort-weighted average treatment effect on the treated (ATT) is -0.0399513 (SE: 0.0119411; 95% CI: [-0.0633554, -0.0165472]). The estimates indicate that negative effects on teen employment emerge and intensify in subsequent years following adoption.",
       "caveats": [
         "Pre-treatment parallel trends are supported: the cohort-weighted average placebo effect prior to adoption (pretrend_mean) is -0.00059695767393341 (bootstrap SE: 0.00682527115146348).",
         "Dynamic effects at event times e = 2 and e = 3 rely solely on the 2004 cohort (20 counties), so longer-run estimates reflect this single early-adopting cohort rather than all adopters.",
         "Standard errors are estimated via a unit bootstrap with 199 replications clustered at the county level."
       ],
       "figure_refs": [
         "call_a3b16404:att"
       ]
     }

   Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
8. 7Guarda
   resposta aceita

   Números encontrados

   2021 · 309 · -3 · 3 · -3 · 0.0305067 · 0.0148852 · 1 · -2 · -0.000563085 · 0.0136651 · 2 · -1 · -0.0244587 · 0.0145633 · 2 · 0 · -0.0199318 · 0.0120377 · 3 · 1 · -0.0509574 · 0.0168004 · 2 · 2 · -0.137259 · 0.0377078 · 1 · 3 · -0.100811 · 0.0349882 · 1 · -0.0399513 · 0.0119411 · 95% · -0.0633554 · -0.0165472 · -0.00059695767393341 · 0.00682527115146348 · 2 · 3 · 2004 · 20 · 199

   Sem lastro

   —

   Não citados

   —

   1 nova(s) tentativa(s) restante(s)
9. 8Resposta

   Using the Callaway and Sant'Anna (2021) estimator with the 309 never-treated counties as the comparison group, the dynamic event-study treatment effects of minimum wage increases on log county teen employment (lemp) from three years before adoption (e = -3) to three years after (e = 3) are: Pre-treatment dynamic effects (placebo/lead tests): - e = -3: 0.0305067 (SE: 0.0148852, 1 cohort) - e = -2: -0.000563085 (SE: 0.0136651, 2 cohorts) - e = -1: -0.0244587 (SE: 0.0145633, 2 cohorts) Post-treatment dynamic effects: - e = 0: -0.0199318 (SE: 0.0120377, 3 cohorts) - e = 1: -0.0509574 (SE: 0.0168004, 2 cohorts) - e = 2: -0.137259 (SE: 0.0377078, 1 cohort) - e = 3: -0.100811 (SE: 0.0349882, 1 cohort) Across all post-adoption cohort-periods, the overall cohort-weighted average treatment effect on the treated (ATT) is -0.0399513 (SE: 0.0119411; 95% CI: [-0.0633554, -0.0165472]). The estimates indicate that negative effects on teen employment emerge and intensify in subsequent years following adoption. Ressalvas: Pre-treatment parallel trends are supported: the cohort-weighted average placebo effect prior to adoption (pretrend_mean) is -0.00059695767393341 (bootstrap SE: 0.00682527115146348). Dynamic effects at event times e = 2 and e = 3 rely solely on the 2004 cohort (20 counties), so longer-run estimates reflect this single early-adopting cohort rather than all adopters. Standard errors are estimated via a unit bootstrap with 199 replications clustered at the county level.

   • Pre-treatment parallel trends are supported: the cohort-weighted average placebo effect prior to adoption (pretrend_mean) is -0.00059695767393341 (bootstrap SE: 0.00682527115146348).
   • Dynamic effects at event times e = 2 and e = 3 rely solely on the 2004 cohort (20 counties), so longer-run estimates reflect this single early-adopting cohort rather than all adopters.
   • Standard errors are estimated via a unit bootstrap with 199 replications clustered at the county level.

   Figuras citadas: call_a3b16404:att · Parada: answered

   Figura da resposta: call_a3b16404:att = -0,0399513-0.03995127515517701 (se 0,01194110.011941084528484509)
10. 9Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
11. 10Pontuação
    #1figura da resposta escolhidapassouobrigatóriaanswer figure call_a3b16404:att (infer.did_staggered)
12. 11Pontuação
    #2ferramenta chamadapassouobrigatóriaanswer call is infer.did_staggered
13. 12Pontuação
    #3argumento igualpassouobrigatóriainfer.did_staggered.y = 'lemp'
14. 13Pontuação
    #4argumento igualpassouobrigatóriainfer.did_staggered.first_treat = 'first_treat'
15. 14Pontuação
    #5argumento igualpassouobrigatóriainfer.did_staggered.unit = 'countyreal'
16. 15Pontuação
    #6argumento igualpassouobrigatóriainfer.did_staggered.control_group = 'never_treated'
17. 16Pontuação
    #7diagnósticopassouobrigatória500 vs 500
18. 17Pontuação
    #8célula de tabela próximapassouobrigatóriaevent_time[{'e': -3}].att = 0.03050665558 vs 0.030506655583292096
19. 18Pontuação
    #9célula de tabela próximapassouregistradaevent_time[{'e': -3}].att = 0.03050665558 vs 0.0305
20. 19Pontuação
    #10célula de tabela próximafalhouregistradaevent_time[{'e': -3}].se = 0.0148852205 vs 0.0154
21. 20Pontuação
    #11célula de tabela próximapassouobrigatóriaevent_time[{'e': -2}].att = -0.0005630846264 vs -0.0005630846263852302
22. 21Pontuação
    #12célula de tabela próximapassouregistradaevent_time[{'e': -2}].att = -0.0005630846264 vs -0.0006
23. 22Pontuação
    #13célula de tabela próximafalhouregistradaevent_time[{'e': -2}].se = 0.01366505846 vs 0.0132
24. 23Pontuação
    #14célula de tabela próximapassouobrigatóriaevent_time[{'e': -1}].att = -0.02445874497 vs -0.024458744971168973
25. 24Pontuação
    #15célula de tabela próximapassouregistradaevent_time[{'e': -1}].att = -0.02445874497 vs -0.0245
26. 25Pontuação
    #16célula de tabela próximafalhouregistradaevent_time[{'e': -1}].se = 0.01456333517 vs 0.0153
27. 26Pontuação
    #17célula de tabela próximapassouobrigatóriaevent_time[{'e': 0}].att = -0.01993181679 vs -0.01993181678925978
28. 27Pontuação
    #18célula de tabela próximapassouregistradaevent_time[{'e': 0}].att = -0.01993181679 vs -0.0199
29. 28Pontuação
    #19célula de tabela próximafalhouregistradaevent_time[{'e': 0}].se = 0.01203765078 vs 0.0123
30. 29Pontuação
    #20célula de tabela próximapassouobrigatóriaevent_time[{'e': 1}].att = -0.05095736707 vs -0.050957367065194985
31. 30Pontuação
    #21célula de tabela próximapassouregistradaevent_time[{'e': 1}].att = -0.05095736707 vs -0.051
32. 31Pontuação
    #22célula de tabela próximafalhouregistradaevent_time[{'e': 1}].se = 0.01680040972 vs 0.0167
33. 32Pontuação
    #23célula de tabela próximapassouobrigatóriaevent_time[{'e': 2}].att = -0.1372587389 vs -0.13725873888940443
34. 33Pontuação
    #24célula de tabela próximapassouregistradaevent_time[{'e': 2}].att = -0.1372587389 vs -0.1373
35. 34Pontuação
    #25célula de tabela próximafalhouregistradaevent_time[{'e': 2}].se = 0.03770782079 vs 0.0413
36. 35Pontuação
    #26célula de tabela próximapassouobrigatóriaevent_time[{'e': 3}].att = -0.1008113631 vs -0.10081136308540525
37. 36Pontuação
    #27célula de tabela próximapassouregistradaevent_time[{'e': 3}].att = -0.1008113631 vs -0.1008
38. 37Pontuação
    #28célula de tabela próximafalhouregistradaevent_time[{'e': 3}].se = 0.03498817563 vs 0.0351

Épocas × verificações

Tentativas

Cada tentativa é seu próprio arquivo e é publicada; só a última de cada época é pontuada.

• época 1, tentativa 1passoupontuada(exibindo)

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