Tarefa crb_papers.q016

Veredito

Pergunta feita ao analista

Among activists who go into exile in month index 56, what is the change in their monthly share of tweets about foreign-led policy topics in the month after exile versus the month before, compared with contemporaneous activists who stayed in-country?

Como o agente resolveu e como foi validado

Resumo passo a passo do que o agente executou, por que o caminho está correto e como cada verificação conferiu o resultado.

1. 1Preparação da base de dados
   ds_0009— | Variable Name | Description | | --- | --- | | actor.id | Stable, URI-style identifier for a public Twitter account belonging to a Venezuelan opposition figure (e.g., legislators, mayors, activists, or journalists). Uniquely identifies the account across all months in the panel. | | month | Month start date (YYYY-MM-01) indicating the calendar month over which tweets were aggregated. Coverage runs from 2013-01-01 through 2020-05-01. | | perc_foreign_policy | Percentage of an account’s tweets in the given month that reference foreign-led policy topics (e.g., diplomacy, sanctions, international pressure, or military action). Constructed via dictionary-based text classification informed by word-embedding similarity trained on the corpus; reported as a percent (0–100), with fractional values possible (e.g., 0.78 means 0.78%). | | tweeted_exile | Indicator (0/1) that the individual associated with the account was residing outside Venezuela during the month. Coded from publicly available reporting and biographical sources; months are flagged once a departure is documented and until return, where known. | | log_tweets | Natural logarithm of the total number of public tweets posted by the account in the month, providing a scale-stable measure of activity volume. | | actor | Text identifier for the account (often the same string as actor.id), included for readability and consistency with external sources. | | months | Sequential month index starting at 1 for the first month in the dataset (January 2013) and increasing by one each calendar month. Useful for aligning time across accounts. | | actorid | Numeric panel identifier for the Twitter account, facilitating joins and fixed account indexing across files or analyses. | Dataset Context: This dataset assembles a monthly panel of Twitter activity from prominent Venezuelan opposition figures across a period of intense political and economic turbulence, spanning January 2013 to May 2020. The roster includes elected officials and well-known public voices who were active on the platform. Researchers identified accounts manually and used Twitter’s full-archive access to retrieve complete public tweet histories. Tweets were then aggregated to the account–month level to create consistent longitudinal records of online discourse. To track whether individuals were inside or outside the country over time, residency status was coded from publicly available sources such as news reports, official statements, and biographical materials. Where exact dates were uncertain, the earliest documented indication of living abroad was used to anchor timing. The monthly topic measure included here—the share of tweets about foreign-led policy actions—was built using dictionaries expanded from seed terms via word-embedding similarity trained on the study corpus, capturing Venezuela-specific vocabulary and multilingual usage. Monthly tweet counts were also recorded and log-transformed to summarize posting volume. The observation window covers major developments in Venezuela’s political landscape, including leadership transitions, contested elections, large protest cycles, and deepening economic and humanitarian crises. During these years, traditional media faced heavy constraints, and social media—particularly Twitter—served as a key venue for public communication, political messaging, and engagement with domestic and international audiences. Many opposition figures maintained active online presences throughout the period, and some relocated abroad; the panel structure allows their communication patterns to be followed continuously month by month. All data derive from public digital traces and public-domain biographical information, and the panel includes only months in which an account posted at least one tweet.
   ref_first
   ref_obs
   ref_panel
   Transformação "select" aplicada à base "ref_first" (22491 → 22491 linhas). {"columns":["actorid","months","tweeted_exile","perc_foreign_policy","log_tweets"]}
   Transformação "dropna" aplicada à base "ref_first" (22491 → 22491 linhas). {"columns":["perc_foreign_policy","log_tweets","months"]}
   Transformação "filter" aplicada à base "ref_first" (22491 → 2754 linhas). {"query":"tweeted_exile == 1"}
   Transformação "collapse" aplicada à base "ref_first" (2754 → 89 linhas). {"aggs":[{"column":"months","fn":"min","name":"g"}],"by":["actorid"]}
   Transformação "select" aplicada à base "ref_obs" (22491 → 22491 linhas). {"columns":["actorid","months","tweeted_exile","perc_foreign_policy","log_tweets"]}
   Transformação "dropna" aplicada à base "ref_obs" (22491 → 22491 linhas). {"columns":["perc_foreign_policy","log_tweets","months"]}
   Transformação "filter" aplicada à base "ref_obs" (22491 → 546 linhas). {"query":"months in [55, 57]"}
   Transformação "collapse" aplicada à base "ref_obs" (546 → 546 linhas). {"aggs":[{"column":"perc_foreign_policy","fn":"count","name":"n"},{"column":"tweeted_exile","fn":"max","name":"t"}],"by":["actorid","months"]}
   Transformação "collapse" aplicada à base "ref_obs" (546 → 282 linhas). {"aggs":[{"column":"n","fn":"count","name":"nboth"},{"column":"t","fn":"max","name":"tmax"}],"by":["actorid"]}
   Transformação "select" aplicada à base "ref_panel" (22491 → 22491 linhas). {"columns":["actorid","months","tweeted_exile","perc_foreign_policy","log_tweets"]}
   Transformação "dropna" aplicada à base "ref_panel" (22491 → 22491 linhas). {"columns":["perc_foreign_policy","log_tweets","months"]}
   Transformação "filter" aplicada à base "ref_panel" (22491 → 546 linhas). {"query":"months in [55, 57]"}
   Transformação "merge" aplicada à base "ref_panel" (546 → 546 linhas). {"how":"left","on":["actorid"],"with":"ref_first"}
   Transformação "merge" aplicada à base "ref_panel" (546 → 546 linhas). {"how":"left","on":["actorid"],"with":"ref_obs"}
   Transformação "filter" aplicada à base "ref_panel" (546 → 472 linhas). {"query":"nboth == 2 and (g == 56 or ((g != g or g > 57) and tmax == 0))"}
   Transformação "derive" aplicada à base "ref_panel" (472 → 472 linhas). {"args":{"cmp":"eq","column":"g","value":56},"name":"treated","op":"indicator"}
   Transformação "derive" aplicada à base "ref_panel" (472 → 472 linhas). {"args":{"column":"treated","default":0,"keep_unmapped":false,"mapping":[{"source":1,"target":1}]},"name":"treated","op":"map"}
   Transformação "derive" aplicada à base "ref_panel" (472 → 472 linhas). {"args":{"cmp":"eq","column":"months","value":57},"name":"post","op":"indicator"}
   Transformação "derive" aplicada à base "ref_panel" (472 → 472 linhas). {"args":{"columns":["treated","post"]},"name":"treated_post","op":"interaction"}
2. 2Chamada que gerou a resposta finalinfer.ols(Regressão linear por mínimos quadrados ordinários (MQO))citada na respostacoincide com a referência

   Ver chamada call_e2c5132e nos detalhes

   sm.OLS(y, [const, treated, post, treated_post, log_tweets]).fit(cov_type='cluster', groups=actorid) (CR1 errors with statsmodels' default G/(G-1)·(N-1)/(N-K) correction)

   Por que este é o caminho de referência: sm.OLS(y, [const, treated, post, treated_post, log_tweets]).fit(cov_type='cluster', groups=actorid) (CR1 errors with statsmodels' default G/(G-1)·(N-1)/(N-K) correction)
   Argumentos principais

   cluster:

   "actorid"

   controls:

   ["treated","post","log_tweets"]

   dataset:

   "ref_panel"

   intercept:

   true

   level:

   0,950.95

   se:

   "HC1"

   x:

   ["treated_post"]

   y:

   "perc_foreign_policy"

   Resultado calculado

   • coef.treated_post = 7,09724 7.097237775872289
     Coefficient on treated_post in the regression of perc_foreign_policy
   Diagnósticos: n_obs = 472472 (observations used) · k = 55 (parameters estimated, fixed-effect dummies included) · r2 = 0,05986390.05986385995481491 · df_resid = 467467 · se_type = "cluster" (cluster-robust standard errors (CR1) by 'actorid') · n_clusters = 236236 (236 clusters of 'actorid')
3. 3Conferência de lastro e resposta final

   Todos os 1 números escritos na resposta foram conferidos contra as ferramentas e o enunciado (nenhum número sem lastro).

   Números encontrados: 7,097
   Figuras citadas: call_e2c5132e:coef.treated_post
   • ✓
     #0resposta com lastro

     every number traced to a tool call or the question
   • ✓
     #1figura da resposta próxima

     CRB estimate_within_solution_ci: the answer figure inside the gold 95% interval effect ± 1.96·|SE| of source crb_papers.q016.gold (closed, evaluate_result.py; no --auto-rescale)
   • ✓
     #2figura da resposta próxima

     replication cell: the gold effect to rel 1e-6 (abs floor 1e-6·SE); never required
   • ✓
     #3figura da resposta próxima

     replication cell: the gold SE to rel 1e-6; never required

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

   Among activists who go into exile in month index 56, what is the change in their monthly share of tweets about foreign-led policy topics in the month after exile versus the month before, compared with contemporaneous activists who stayed in-country?

   ds_0009 (agent)

   | Variable Name | Description | | --- | --- | | actor.id | Stable, URI-style identifier for a public Twitter account belonging to a Venezuelan opposition figure (e.g., legislators, mayors, activists, or journalists). Uniquely identifies the account across all months in the panel. | | month | Month start date (YYYY-MM-01) indicating the calendar month over which tweets were aggregated. Coverage runs from 2013-01-01 through 2020-05-01. | | perc_foreign_policy | Percentage of an account’s tweets in the given month that reference foreign-led policy topics (e.g., diplomacy, sanctions, international pressure, or military action). Constructed via dictionary-based text classification informed by word-embedding similarity trained on the corpus; reported as a percent (0–100), with fractional values possible (e.g., 0.78 means 0.78%). | | tweeted_exile | Indicator (0/1) that the individual associated with the account was residing outside Venezuela during the month. Coded from publicly available reporting and biographical sources; months are flagged once a departure is documented and until return, where known. | | log_tweets | Natural logarithm of the total number of public tweets posted by the account in the month, providing a scale-stable measure of activity volume. | | actor | Text identifier for the account (often the same string as actor.id), included for readability and consistency with external sources. | | months | Sequential month index starting at 1 for the first month in the dataset (January 2013) and increasing by one each calendar month. Useful for aligning time across accounts. | | actorid | Numeric panel identifier for the Twitter account, facilitating joins and fixed account indexing across files or analyses. | Dataset Context: This dataset assembles a monthly panel of Twitter activity from prominent Venezuelan opposition figures across a period of intense political and economic turbulence, spanning January 2013 to May 2020. The roster includes elected officials and well-known public voices who were active on the platform. Researchers identified accounts manually and used Twitter’s full-archive access to retrieve complete public tweet histories. Tweets were then aggregated to the account–month level to create consistent longitudinal records of online discourse. To track whether individuals were inside or outside the country over time, residency status was coded from publicly available sources such as news reports, official statements, and biographical materials. Where exact dates were uncertain, the earliest documented indication of living abroad was used to anchor timing. The monthly topic measure included here—the share of tweets about foreign-led policy actions—was built using dictionaries expanded from seed terms via word-embedding similarity trained on the study corpus, capturing Venezuela-specific vocabulary and multilingual usage. Monthly tweet counts were also recorded and log-transformed to summarize posting volume. The observation window covers major developments in Venezuela’s political landscape, including leadership transitions, contested elections, large protest cycles, and deepening economic and humanitarian crises. During these years, traditional media faced heavy constraints, and social media—particularly Twitter—served as a key venue for public communication, political messaging, and engagement with domestic and international audiences. Many opposition figures maintained active online presences throughout the period, and some relocated abroad; the panel structure allows their communication patterns to be followed continuously month by month. All data derive from public digital traces and public-domain biographical information, and the panel includes only months in which an account posted at least one tweet.

   file 3128de21a97807498f279bcb7159973e89621cc6bd4a98c8f319813649f925a8 · frame —

   ref_first (reference)

   file 3128de21a97807498f279bcb7159973e89621cc6bd4a98c8f319813649f925a8 · frame —

   1. select {"columns":["actorid","months","tweeted_exile","perc_foreign_policy","log_tweets"]}
   2. dropna {"columns":["perc_foreign_policy","log_tweets","months"]}
   3. filter {"query":"tweeted_exile == 1"}
   4. collapse {"aggs":[{"column":"months","fn":"min","name":"g"}],"by":["actorid"]}

   ref_obs (reference)

   file 3128de21a97807498f279bcb7159973e89621cc6bd4a98c8f319813649f925a8 · frame —

   1. select {"columns":["actorid","months","tweeted_exile","perc_foreign_policy","log_tweets"]}
   2. dropna {"columns":["perc_foreign_policy","log_tweets","months"]}
   3. filter {"query":"months in [55, 57]"}
   4. collapse {"aggs":[{"column":"perc_foreign_policy","fn":"count","name":"n"},{"column":"tweeted_exile","fn":"max","name":"t"}],"by":["actorid","months"]}
   5. collapse {"aggs":[{"column":"n","fn":"count","name":"nboth"},{"column":"t","fn":"max","name":"tmax"}],"by":["actorid"]}

   ref_panel (reference)

   file 3128de21a97807498f279bcb7159973e89621cc6bd4a98c8f319813649f925a8 · frame —

   1. select {"columns":["actorid","months","tweeted_exile","perc_foreign_policy","log_tweets"]}
   2. dropna {"columns":["perc_foreign_policy","log_tweets","months"]}
   3. filter {"query":"months in [55, 57]"}
   4. merge {"how":"left","on":["actorid"],"with":"ref_first"}
   5. merge {"how":"left","on":["actorid"],"with":"ref_obs"}
   6. filter {"query":"nboth == 2 and (g == 56 or ((g != g or g > 57) and tmax == 0))"}
   7. derive {"args":{"cmp":"eq","column":"g","value":56},"name":"treated","op":"indicator"}
   8. derive {"args":{"column":"treated","default":0,"keep_unmapped":false,"mapping":[{"source":1,"target":1}]},"name":"treated","op":"map"}
   9. derive {"args":{"cmp":"eq","column":"months","value":57},"name":"post","op":"indicator"}
   10. derive {"args":{"columns":["treated","post"]},"name":"treated_post","op":"interaction"}
2. 1Transformação
   ref_first · select {"columns":["actorid","months","tweeted_exile","perc_foreign_policy","log_tweets"]}

   22491 → 22491 linhas

   Frame antes → depois: bedb4686cb9f → 8c26e880509f
3. 2Transformação
   ref_first · dropna {"columns":["perc_foreign_policy","log_tweets","months"]}

   22491 → 22491 linhas

   Frame antes → depois: 8c26e880509f → 8c26e880509f
4. 3Transformação
   ref_first · filter {"query":"tweeted_exile == 1"}

   22491 → 2754 linhas

   Frame antes → depois: 8c26e880509f → 4cad94d432f5
5. 4Transformação
   ref_first · collapse {"aggs":[{"column":"months","fn":"min","name":"g"}],"by":["actorid"]}

   2754 → 89 linhas

   Frame antes → depois: 4cad94d432f5 → c717ff2e6bc0
6. 5Transformação
   ref_obs · select {"columns":["actorid","months","tweeted_exile","perc_foreign_policy","log_tweets"]}

   22491 → 22491 linhas

   Frame antes → depois: bedb4686cb9f → 8c26e880509f
7. 6Transformação
   ref_obs · dropna {"columns":["perc_foreign_policy","log_tweets","months"]}

   22491 → 22491 linhas

   Frame antes → depois: 8c26e880509f → 8c26e880509f
8. 7Transformação
   ref_obs · filter {"query":"months in [55, 57]"}

   22491 → 546 linhas

   Frame antes → depois: 8c26e880509f → a9fe0fa41a21
9. 8Transformação
   ref_obs · collapse {"aggs":[{"column":"perc_foreign_policy","fn":"count","name":"n"},{"column":"tweeted_exile","fn":"max","name":"t"}],"by":["actorid","months"]}

   546 → 546 linhas

   Frame antes → depois: a9fe0fa41a21 → 48330a3855a7
10. 9Transformação
    ref_obs · collapse {"aggs":[{"column":"n","fn":"count","name":"nboth"},{"column":"t","fn":"max","name":"tmax"}],"by":["actorid"]}

    546 → 282 linhas

    Frame antes → depois: 48330a3855a7 → eb958d258fd8
11. 10Transformação
    ref_panel · select {"columns":["actorid","months","tweeted_exile","perc_foreign_policy","log_tweets"]}

    22491 → 22491 linhas

    Frame antes → depois: bedb4686cb9f → 8c26e880509f
12. 11Transformação
    ref_panel · dropna {"columns":["perc_foreign_policy","log_tweets","months"]}

    22491 → 22491 linhas

    Frame antes → depois: 8c26e880509f → 8c26e880509f
13. 12Transformação
    ref_panel · filter {"query":"months in [55, 57]"}

    22491 → 546 linhas

    Frame antes → depois: 8c26e880509f → a9fe0fa41a21
14. 13Transformação
    ref_panel · merge {"how":"left","on":["actorid"],"with":"ref_first"}

    546 → 546 linhas

    Frame antes → depois: a9fe0fa41a21 → d6071d130dd2
15. 14Transformação
    ref_panel · merge {"how":"left","on":["actorid"],"with":"ref_obs"}

    546 → 546 linhas

    Frame antes → depois: d6071d130dd2 → 751d3676a979
16. 15Transformação
    ref_panel · filter {"query":"nboth == 2 and (g == 56 or ((g != g or g > 57) and tmax == 0))"}

    546 → 472 linhas

    Frame antes → depois: 751d3676a979 → 1402ad5d1dae
17. 16Transformação
    ref_panel · derive {"args":{"cmp":"eq","column":"g","value":56},"name":"treated","op":"indicator"}

    472 → 472 linhas

    Frame antes → depois: 1402ad5d1dae → 61f820dfe4bc
18. 17Transformação
    ref_panel · derive {"args":{"column":"treated","default":0,"keep_unmapped":false,"mapping":[{"source":1,"target":1}]},"name":"treated","op":"map"}

    472 → 472 linhas

    Frame antes → depois: 61f820dfe4bc → e43da9d3620b
19. 18Transformação
    ref_panel · derive {"args":{"cmp":"eq","column":"months","value":57},"name":"post","op":"indicator"}

    472 → 472 linhas

    Frame antes → depois: e43da9d3620b → ca87501bc33d
20. 19Transformação
    ref_panel · derive {"args":{"columns":["treated","post"]},"name":"treated_post","op":"interaction"}

    472 → 472 linhas

    Frame antes → depois: ca87501bc33d → 24455721da48
21. 20Turno do modelo
    call_4ddd801creferenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

    Texto visível

    sm.OLS(y, [const, treated, post, treated_post, log_tweets]).fit(cov_type='cluster', groups=actorid) (CR1 errors with statsmodels' default G/(G-1)·(N-1)/(N-K) correction)

    Chamadas pedidas

    • infer.ols

      {
        "cluster": "actorid",
        "controls": [
          "treated",
          "post",
          "log_tweets"
        ],
        "dataset": "ref_panel",
        "x": [
          "treated_post"
        ],
        "y": "perc_foreign_policy"
      }

    Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
22. 21Ferramenta
    infer.ols— Regressão linear por mínimos quadrados ordinários (MQO)v0.1.0call_e2c5132edecidido pelo modeloreexecutável

    Motivo: sm.OLS(y, [const, treated, post, treated_post, log_tweets]).fit(cov_type='cluster', groups=actorid) (CR1 errors with statsmodels' default G/(G-1)·(N-1)/(N-K) correction)

    Argumentos

    {
      "cluster": "actorid",
      "controls": [
        "treated",
        "post",
        "log_tweets"
      ],
      "dataset": "ref_panel",
      "fe": [],
      "intercept": true,
      "level": 0.95,
      "se": "HC1",
      "subset": null,
      "weights": null,
      "x": [
        "treated_post"
      ],
      "y": "perc_foreign_policy"
    }

    Figuras call_e2c5132e

    Chave	Valor	Valor bruto	Erro-padrão	IC
    coef.treated_post Coefficient on treated_post in the regression of perc_foreign_policy	7,09724	7.097237775872289	2,05872.058699350153437	3,04137 – 11,1531

    Diagnósticos

    • n_obs = 472472 (observations used)
    • k = 55 (parameters estimated, fixed-effect dummies included)
    • r2 = 0,05986390.05986385995481491
    • df_resid = 467467
    • se_type = "cluster" (cluster-robust standard errors (CR1) by 'actorid')
    • n_clusters = 236236 (236 clusters of 'actorid')

    Derivação matemática e pressupostos

    coef.treated_post

    1. Coeficiente de treated_post na regressão por mínimos quadrados ordinários de perc_foreign_policy.→ 7,09724 (7.097237775872289)
    2. Erro-padrão robusto por conglomerado (CR1): a raiz da variância estimada do coeficiente.ep = √V_jj→ 2,0587 (2.058699350153437)
    3. Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 235 graus de liberdade vezes o erro-padrão.IC = β ± c · ep

    Pressupostos do estimador

    • the conditional mean of the outcome is linear in the regressors
    • errors are independent across clusters of 'actorid' and may be correlated within them

    Tabelas (1)

    {
      "coefficients": [
        {
          "ci_high": 3.856158004212713,
          "ci_low": -1.0105442084808618,
          "coef": 1.4228068978659256,
          "p": 0.2505143053507573,
          "se": 1.2351346013356097,
          "t": 1.1519448134052572,
          "term": "Intercept"
        },
        {
          "ci_high": 11.153102080794381,
          "ci_low": 3.0413734709501963,
          "coef": 7.097237775872289,
          "p": 0.000670417417377135,
          "se": 2.058699350153437,
          "t": 3.447437711263339,
          "term": "treated_post"
        },
        {
          "ci_high": 0.12225785146870893,
          "ci_low": -1.5301914138229542,
          "coef": -0.7039667811771226,
          "p": 0.09456126476844132,
          "se": 0.419379936415651,
          "t": -1.6785895557946175,
          "term": "treated"
        },
        {
          "ci_high": 1.7545143938040368,
          "ci_low": 0.6646019268112816,
          "coef": 1.2095581603076593,
          "p": 0.00001843901533273804,
          "se": 0.2766120756060668,
          "t": 4.3727597851882445,
          "term": "post"
        },
        {
          "ci_high": 0.40700272031897883,
          "ci_low": -0.499242719862586,
          "coef": -0.04611999977180357,
          "p": 0.8412450918119087,
          "se": 0.22999868320510053,
          "t": -0.2005228861709448,
          "term": "log_tweets"
        }
      ]
    }

    Hashes SHA-256 e metadados de reprodução

    Digests dos dados

    ref_panel 24455721da48467cdc22a318c77ed00880996e402036d9341030b977c35ff307

    sha256 do resultado

    47c0f5ad84a6a812cbdf96abd26178620d57fdc39e3ef97f1dee9f9c5c964cb3

    Resultado mostrado ao modelo
23. 22Turno do modelo
    call_55802d32referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

    Chamadas pedidas

    • submit_answer (meta-ferramenta)

      {
        "answer": "Estimativa (infer.ols): coef.treated_post = 7,097. Reference path transcribed from the item's own estimation script.",
        "caveats": [],
        "figure_refs": [
          "call_e2c5132e:coef.treated_post"
        ]
      }

    Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
24. 23Guarda
    resposta aceita

    Números encontrados

    7,097

    Sem lastro

    —

    Não citados

    —

    1 nova(s) tentativa(s) restante(s)
25. 24Resposta

    Estimativa (infer.ols): coef.treated_post = 7,097. Reference path transcribed from the item's own estimation script.

    Figuras citadas: call_e2c5132e:coef.treated_post · Parada: answered

    Figura da resposta: call_e2c5132e:coef.treated_post = 7,097247.097237775872289 (se 2,05872.058699350153437)
26. 25Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
27. 26Pontuação
    #1figura da resposta próximapassouobrigatória7.097237776 in [3.06218705, 11.1322885]?
28. 27Pontuação
    #2figura da resposta próximapassouregistrada7.097237776 vs 7.097237775872275
29. 28Pontuação
    #3figura da resposta próximapassouregistrada2.05869935 vs 2.058699350153441

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)

Downloads desta tarefa