Tarefa crb_papers.q001

Veredito

Pergunta feita ao analista

For countries that newly experienced at least one fatal armed conflict in 2000-2002, how did the log of annual non-fuel mineral exploration investment change relative to countries that remained conflict-free in both 1997-1999 and 2000-2002?

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_0001— | Variable Name | Description | | --- | --- | | dv | Natural log of total annual exploration spending on non‑fuel minerals (USD, constant 1997) plus one, aggregated to the country–year. Figures come from SNL Metals & Mining’s compilation of company surveys and reports and cover activities such as drilling, geophysics, sampling, assaying, and feasibility studies for metals and minerals (e.g., copper, gold, diamonds, iron ore, silver, uranium). Values are deflated to 1997 dollars and summed across firms within each country and year; the log(+1) form preserves observations with zero reported spending. | | ucdp_lead | Indicator equal to 1 if the UCDP Georeferenced Event Dataset records at least one fatal armed conflict event within the country in the current calendar year or the previous year; 0 otherwise. UCDP defines events as organized use of armed force resulting in at least one direct death and classifies state‑based, one‑sided, and non‑state violence. | | country | Country or territory corresponding to the observation. The panel spans a broad global sample (about 177 countries). | | year | Calendar year of the observation (1997–2014). Stored numerically; represents the Gregorian year. | Dataset Context: This global panel links country–year exploration budgets in the mining sector with standardized information on the presence of armed conflict. Exploration spending is compiled by SNL Metals & Mining from company surveys and public financial reports, then aggregated from firm-level entries to the country–year level. The series covers commercially oriented exploration for non‑fuel minerals—such as base and precious metals, diamonds, iron ore, rare earths, and uranium—and excludes hydrocarbons. Expenditures typically encompass early‑stage and appraisal activities (e.g., drilling campaigns, geophysical surveys, sampling and assaying, scoping and feasibility studies). SNL’s process captures the vast majority of global nonferrous exploration outlays and excludes very small projects below common reporting thresholds; amounts are converted to constant 1997 USD to remove the effects of inflation. Because many country–years legitimately report zero exploration, the spending measure is stored as ln(expenditure + 1). Conflict information is sourced from the Uppsala Conflict Data Program’s georeferenced event data, which catalogs individual incidents where organized actors use armed force resulting in at least one direct fatality. Events include state‑based battles, one‑sided violence against civilians, and non‑state clashes. For each country–year, the dataset flags whether any fatal incident occurred in that year or the year immediately prior, capturing near‑term conflict conditions that overlap with annual financial reporting cycles. The 1997–2014 window spans substantial swings in global metals prices and investment appetites, including the mid‑2000s commodity upcycle and subsequent downturn, and includes both “junior” and “major” mining companies whose exploration budgets respond to financing conditions and market sentiment. The resulting dataset provides harmonized, inflation‑adjusted coverage of exploration activity across roughly 177 countries aligned with a consistent, event‑based record of armed conflict incidence.
   ref_panel
   ref_post
   ref_pre
   Transformação "select" aplicada à base "ref_pre" (3186 → 3186 linhas). {"columns":["country","year","dv","ucdp_lead"]}
   Transformação "dropna" aplicada à base "ref_pre" (3186 → 3186 linhas). {"columns":["country","year","dv","ucdp_lead"]}
   Transformação "derive" aplicada à base "ref_pre" (3186 → 3186 linhas). {"args":{"cmp":"gt","column":"ucdp_lead","value":0},"name":"ucdp_lead","op":"indicator"}
   Transformação "filter" aplicada à base "ref_pre" (3186 → 531 linhas). {"query":"year in [1997, 1998, 1999]"}
   Transformação "collapse" aplicada à base "ref_pre" (531 → 177 linhas). {"aggs":[{"column":"dv","fn":"mean","name":"y0"},{"column":"ucdp_lead","fn":"mean","name":"d_pre"}],"by":["country"]}
   Transformação "select" aplicada à base "ref_post" (3186 → 3186 linhas). {"columns":["country","year","dv","ucdp_lead"]}
   Transformação "dropna" aplicada à base "ref_post" (3186 → 3186 linhas). {"columns":["country","year","dv","ucdp_lead"]}
   Transformação "derive" aplicada à base "ref_post" (3186 → 3186 linhas). {"args":{"cmp":"gt","column":"ucdp_lead","value":0},"name":"ucdp_lead","op":"indicator"}
   Transformação "filter" aplicada à base "ref_post" (3186 → 531 linhas). {"query":"year in [2000, 2001, 2002]"}
   Transformação "collapse" aplicada à base "ref_post" (531 → 177 linhas). {"aggs":[{"column":"dv","fn":"mean","name":"y1"},{"column":"ucdp_lead","fn":"mean","name":"d_post"}],"by":["country"]}
   Transformação "select" aplicada à base "ref_panel" (3186 → 3186 linhas). {"columns":["country","year","dv","ucdp_lead"]}
   Transformação "dropna" aplicada à base "ref_panel" (3186 → 3186 linhas). {"columns":["country","year","dv","ucdp_lead"]}
   Transformação "derive" aplicada à base "ref_panel" (3186 → 3186 linhas). {"args":{"cmp":"gt","column":"ucdp_lead","value":0},"name":"ucdp_lead","op":"indicator"}
   Transformação "filter" aplicada à base "ref_panel" (3186 → 1062 linhas). {"query":"year in [1997, 1998, 1999, 2000, 2001, 2002]"}
   Transformação "collapse" aplicada à base "ref_panel" (1062 → 177 linhas). {"aggs":[{"column":"dv","fn":"count","name":"n"}],"by":["country"]}
   Transformação "select" aplicada à base "ref_panel" (177 → 177 linhas). {"columns":["country"]}
   Transformação "merge" aplicada à base "ref_panel" (177 → 177 linhas). {"how":"inner","on":["country"],"with":"ref_pre"}
   Transformação "merge" aplicada à base "ref_panel" (177 → 177 linhas). {"how":"inner","on":["country"],"with":"ref_post"}
   Transformação "filter" aplicada à base "ref_panel" (177 → 107 linhas). {"query":"d_pre == 0 and (d_post > 0 or d_post == 0)"}
   Transformação "derive" aplicada à base "ref_panel" (107 → 107 linhas). {"args":{"cmp":"gt","column":"d_post","value":0},"name":"treated","op":"indicator"}
   Transformação "reshape_long" aplicada à base "ref_panel" (107 → 214 linhas). {"id":["country"],"j":"post","stubs":["y"]}
   Transformação "derive" aplicada à base "ref_panel" (214 → 214 linhas). {"args":{"columns":["treated","post"]},"name":"interaction","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_31d40207 nos detalhes

   sm.OLS(y ~ const + treated + post + interaction), cov_type='cluster' by country

   Por que este é o caminho de referência: sm.OLS(y ~ const + treated + post + interaction), cov_type='cluster' by country
   Argumentos principais

   cluster:

   "country"

   controls:

   ["treated","post"]

   dataset:

   "ref_panel"

   intercept:

   true

   level:

   0,950.95

   se:

   "HC1"

   x:

   ["interaction"]

   y:

   "y"

   Resultado calculado

   • coef.interaction = -0,0834813 -0.0834813334522015
     Coefficient on interaction in the regression of y
   Diagnósticos: n_obs = 214214 (observations used) · k = 44 (parameters estimated, fixed-effect dummies included) · r2 = 0,05486090.054860876876529185 · df_resid = 210210 · se_type = "cluster" (cluster-robust standard errors (CR1) by 'country') · n_clusters = 107107 (107 clusters of 'country')
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: -0,08348
   Figuras citadas: call_31d40207:coef.interaction
   • ✓
     #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.q001.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

   For countries that newly experienced at least one fatal armed conflict in 2000-2002, how did the log of annual non-fuel mineral exploration investment change relative to countries that remained conflict-free in both 1997-1999 and 2000-2002?

   ds_0001 (agent)

   | Variable Name | Description | | --- | --- | | dv | Natural log of total annual exploration spending on non‑fuel minerals (USD, constant 1997) plus one, aggregated to the country–year. Figures come from SNL Metals & Mining’s compilation of company surveys and reports and cover activities such as drilling, geophysics, sampling, assaying, and feasibility studies for metals and minerals (e.g., copper, gold, diamonds, iron ore, silver, uranium). Values are deflated to 1997 dollars and summed across firms within each country and year; the log(+1) form preserves observations with zero reported spending. | | ucdp_lead | Indicator equal to 1 if the UCDP Georeferenced Event Dataset records at least one fatal armed conflict event within the country in the current calendar year or the previous year; 0 otherwise. UCDP defines events as organized use of armed force resulting in at least one direct death and classifies state‑based, one‑sided, and non‑state violence. | | country | Country or territory corresponding to the observation. The panel spans a broad global sample (about 177 countries). | | year | Calendar year of the observation (1997–2014). Stored numerically; represents the Gregorian year. | Dataset Context: This global panel links country–year exploration budgets in the mining sector with standardized information on the presence of armed conflict. Exploration spending is compiled by SNL Metals & Mining from company surveys and public financial reports, then aggregated from firm-level entries to the country–year level. The series covers commercially oriented exploration for non‑fuel minerals—such as base and precious metals, diamonds, iron ore, rare earths, and uranium—and excludes hydrocarbons. Expenditures typically encompass early‑stage and appraisal activities (e.g., drilling campaigns, geophysical surveys, sampling and assaying, scoping and feasibility studies). SNL’s process captures the vast majority of global nonferrous exploration outlays and excludes very small projects below common reporting thresholds; amounts are converted to constant 1997 USD to remove the effects of inflation. Because many country–years legitimately report zero exploration, the spending measure is stored as ln(expenditure + 1). Conflict information is sourced from the Uppsala Conflict Data Program’s georeferenced event data, which catalogs individual incidents where organized actors use armed force resulting in at least one direct fatality. Events include state‑based battles, one‑sided violence against civilians, and non‑state clashes. For each country–year, the dataset flags whether any fatal incident occurred in that year or the year immediately prior, capturing near‑term conflict conditions that overlap with annual financial reporting cycles. The 1997–2014 window spans substantial swings in global metals prices and investment appetites, including the mid‑2000s commodity upcycle and subsequent downturn, and includes both “junior” and “major” mining companies whose exploration budgets respond to financing conditions and market sentiment. The resulting dataset provides harmonized, inflation‑adjusted coverage of exploration activity across roughly 177 countries aligned with a consistent, event‑based record of armed conflict incidence.

   file 887d9adebe0378bfe7ea873256a67275c50fc96aab151431c32de798d1756d41 · frame —

   ref_panel (reference)

   file 887d9adebe0378bfe7ea873256a67275c50fc96aab151431c32de798d1756d41 · frame —

   1. select {"columns":["country","year","dv","ucdp_lead"]}
   2. dropna {"columns":["country","year","dv","ucdp_lead"]}
   3. derive {"args":{"cmp":"gt","column":"ucdp_lead","value":0},"name":"ucdp_lead","op":"indicator"}
   4. filter {"query":"year in [1997, 1998, 1999, 2000, 2001, 2002]"}
   5. collapse {"aggs":[{"column":"dv","fn":"count","name":"n"}],"by":["country"]}
   6. select {"columns":["country"]}
   7. merge {"how":"inner","on":["country"],"with":"ref_pre"}
   8. merge {"how":"inner","on":["country"],"with":"ref_post"}
   9. filter {"query":"d_pre == 0 and (d_post > 0 or d_post == 0)"}
   10. derive {"args":{"cmp":"gt","column":"d_post","value":0},"name":"treated","op":"indicator"}
   11. reshape_long {"id":["country"],"j":"post","stubs":["y"]}
   12. derive {"args":{"columns":["treated","post"]},"name":"interaction","op":"interaction"}

   ref_post (reference)

   file 887d9adebe0378bfe7ea873256a67275c50fc96aab151431c32de798d1756d41 · frame —

   1. select {"columns":["country","year","dv","ucdp_lead"]}
   2. dropna {"columns":["country","year","dv","ucdp_lead"]}
   3. derive {"args":{"cmp":"gt","column":"ucdp_lead","value":0},"name":"ucdp_lead","op":"indicator"}
   4. filter {"query":"year in [2000, 2001, 2002]"}
   5. collapse {"aggs":[{"column":"dv","fn":"mean","name":"y1"},{"column":"ucdp_lead","fn":"mean","name":"d_post"}],"by":["country"]}

   ref_pre (reference)

   file 887d9adebe0378bfe7ea873256a67275c50fc96aab151431c32de798d1756d41 · frame —

   1. select {"columns":["country","year","dv","ucdp_lead"]}
   2. dropna {"columns":["country","year","dv","ucdp_lead"]}
   3. derive {"args":{"cmp":"gt","column":"ucdp_lead","value":0},"name":"ucdp_lead","op":"indicator"}
   4. filter {"query":"year in [1997, 1998, 1999]"}
   5. collapse {"aggs":[{"column":"dv","fn":"mean","name":"y0"},{"column":"ucdp_lead","fn":"mean","name":"d_pre"}],"by":["country"]}
2. 1Transformação
   ref_pre · select {"columns":["country","year","dv","ucdp_lead"]}

   3186 → 3186 linhas

   Frame antes → depois: 6fc175d00f59 → a23840f8ea16
3. 2Transformação
   ref_pre · dropna {"columns":["country","year","dv","ucdp_lead"]}

   3186 → 3186 linhas

   Frame antes → depois: a23840f8ea16 → a23840f8ea16
4. 3Transformação
   ref_pre · derive {"args":{"cmp":"gt","column":"ucdp_lead","value":0},"name":"ucdp_lead","op":"indicator"}

   3186 → 3186 linhas

   Frame antes → depois: a23840f8ea16 → a23840f8ea16
5. 4Transformação
   ref_pre · filter {"query":"year in [1997, 1998, 1999]"}

   3186 → 531 linhas

   Frame antes → depois: a23840f8ea16 → 142e9561d859
6. 5Transformação
   ref_pre · collapse {"aggs":[{"column":"dv","fn":"mean","name":"y0"},{"column":"ucdp_lead","fn":"mean","name":"d_pre"}],"by":["country"]}

   531 → 177 linhas

   Frame antes → depois: 142e9561d859 → d7feed5e8ef0
7. 6Transformação
   ref_post · select {"columns":["country","year","dv","ucdp_lead"]}

   3186 → 3186 linhas

   Frame antes → depois: 6fc175d00f59 → a23840f8ea16
8. 7Transformação
   ref_post · dropna {"columns":["country","year","dv","ucdp_lead"]}

   3186 → 3186 linhas

   Frame antes → depois: a23840f8ea16 → a23840f8ea16
9. 8Transformação
   ref_post · derive {"args":{"cmp":"gt","column":"ucdp_lead","value":0},"name":"ucdp_lead","op":"indicator"}

   3186 → 3186 linhas

   Frame antes → depois: a23840f8ea16 → a23840f8ea16
10. 9Transformação
    ref_post · filter {"query":"year in [2000, 2001, 2002]"}

    3186 → 531 linhas

    Frame antes → depois: a23840f8ea16 → c3ee4ac54e46
11. 10Transformação
    ref_post · collapse {"aggs":[{"column":"dv","fn":"mean","name":"y1"},{"column":"ucdp_lead","fn":"mean","name":"d_post"}],"by":["country"]}

    531 → 177 linhas

    Frame antes → depois: c3ee4ac54e46 → b99159d2aa0c
12. 11Transformação
    ref_panel · select {"columns":["country","year","dv","ucdp_lead"]}

    3186 → 3186 linhas

    Frame antes → depois: 6fc175d00f59 → a23840f8ea16
13. 12Transformação
    ref_panel · dropna {"columns":["country","year","dv","ucdp_lead"]}

    3186 → 3186 linhas

    Frame antes → depois: a23840f8ea16 → a23840f8ea16
14. 13Transformação
    ref_panel · derive {"args":{"cmp":"gt","column":"ucdp_lead","value":0},"name":"ucdp_lead","op":"indicator"}

    3186 → 3186 linhas

    Frame antes → depois: a23840f8ea16 → a23840f8ea16
15. 14Transformação
    ref_panel · filter {"query":"year in [1997, 1998, 1999, 2000, 2001, 2002]"}

    3186 → 1062 linhas

    Frame antes → depois: a23840f8ea16 → d999769ee1a4
16. 15Transformação
    ref_panel · collapse {"aggs":[{"column":"dv","fn":"count","name":"n"}],"by":["country"]}

    1062 → 177 linhas

    Frame antes → depois: d999769ee1a4 → b336986f44ea
17. 16Transformação
    ref_panel · select {"columns":["country"]}

    177 → 177 linhas

    Frame antes → depois: b336986f44ea → 08a3639bb0e5
18. 17Transformação
    ref_panel · merge {"how":"inner","on":["country"],"with":"ref_pre"}

    177 → 177 linhas

    Frame antes → depois: 08a3639bb0e5 → d7feed5e8ef0
19. 18Transformação
    ref_panel · merge {"how":"inner","on":["country"],"with":"ref_post"}

    177 → 177 linhas

    Frame antes → depois: d7feed5e8ef0 → ab51dba11ec7
20. 19Transformação
    ref_panel · filter {"query":"d_pre == 0 and (d_post > 0 or d_post == 0)"}

    177 → 107 linhas

    Frame antes → depois: ab51dba11ec7 → af858aed933d
21. 20Transformação
    ref_panel · derive {"args":{"cmp":"gt","column":"d_post","value":0},"name":"treated","op":"indicator"}

    107 → 107 linhas

    Frame antes → depois: af858aed933d → 7f6fa963eef5
22. 21Transformação
    ref_panel · reshape_long {"id":["country"],"j":"post","stubs":["y"]}

    107 → 214 linhas

    Frame antes → depois: 7f6fa963eef5 → 9817d5d8a8dc
23. 22Transformação
    ref_panel · derive {"args":{"columns":["treated","post"]},"name":"interaction","op":"interaction"}

    214 → 214 linhas

    Frame antes → depois: 9817d5d8a8dc → 2b9356ec0b9b
24. 23Turno do modelo
    call_393855a1referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

    Texto visível

    sm.OLS(y ~ const + treated + post + interaction), cov_type='cluster' by country

    Chamadas pedidas

    • infer.ols

      {
        "cluster": "country",
        "controls": [
          "treated",
          "post"
        ],
        "dataset": "ref_panel",
        "x": [
          "interaction"
        ],
        "y": "y"
      }

    Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
25. 24Ferramenta
    infer.ols— Regressão linear por mínimos quadrados ordinários (MQO)v0.1.0call_31d40207decidido pelo modeloreexecutável

    Motivo: sm.OLS(y ~ const + treated + post + interaction), cov_type='cluster' by country

    Argumentos

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

    Figuras call_31d40207

    Chave	Valor	Valor bruto	Erro-padrão	IC
    coef.interaction Coefficient on interaction in the regression of y	-0,0834813	-0.0834813334522015	0,4171810.4171809476066827	-0,910583 – 0,74362

    Diagnósticos

    • n_obs = 214214 (observations used)
    • k = 44 (parameters estimated, fixed-effect dummies included)
    • r2 = 0,05486090.054860876876529185
    • df_resid = 210210
    • se_type = "cluster" (cluster-robust standard errors (CR1) by 'country')
    • n_clusters = 107107 (107 clusters of 'country')

    Derivação matemática e pressupostos

    coef.interaction

    1. Coeficiente de interaction na regressão por mínimos quadrados ordinários de y.→ -0,0834813 (-0.0834813334522015)
    2. Erro-padrão robusto por conglomerado (CR1): a raiz da variância estimada do coeficiente.ep = √V_jj→ 0,417181 (0.4171809476066827)
    3. Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 106 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 'country' and may be correlated within them

    Tabelas (1)

    {
      "coefficients": [
        {
          "ci_high": 9.342532612089958,
          "ci_low": 6.364856763953857,
          "coef": 7.853694688021908,
          "p": 5.168148451502389e-18,
          "se": 0.7509532837457076,
          "t": 10.458299947565546,
          "term": "Intercept"
        },
        {
          "ci_high": 0.7436204709335442,
          "ci_low": -0.9105831378379473,
          "coef": -0.0834813334522015,
          "p": 0.8417793598356013,
          "se": 0.4171809476066827,
          "t": -0.2001082118709494,
          "term": "interaction"
        },
        {
          "ci_high": 11.693861258863539,
          "ci_low": 1.9433977612381854,
          "coef": 6.8186295100508625,
          "p": 0.006565168162864022,
          "se": 2.459012651147964,
          "t": 2.7729135540915815,
          "term": "treated"
        },
        {
          "ci_high": -0.18115594914199162,
          "ci_low": -1.600974686324159,
          "coef": -0.8910653177330753,
          "p": 0.014383952558234266,
          "se": 0.358070386901932,
          "t": -2.488519995866398,
          "term": "post"
        }
      ]
    }

    Hashes SHA-256 e metadados de reprodução

    Digests dos dados

    ref_panel 2b9356ec0b9bde6fad91668b9d998d6f995d46c83231f41522dcf118f76c42fa

    sha256 do resultado

    fa985a616e098724bea9c459e2defd64f85ed7b2b67761466ad54c208d0f9f89

    Resultado mostrado ao modelo
26. 25Turno do modelo
    call_ad7b38dereferenceFim: 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.interaction = -0,08348. Reference path transcribed from the item's own estimation script.",
        "caveats": [],
        "figure_refs": [
          "call_31d40207:coef.interaction"
        ]
      }

    Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
27. 26Guarda
    resposta aceita

    Números encontrados

    -0,08348

    Sem lastro

    —

    Não citados

    —

    1 nova(s) tentativa(s) restante(s)
28. 27Resposta

    Estimativa (infer.ols): coef.interaction = -0,08348. Reference path transcribed from the item's own estimation script.

    Figuras citadas: call_31d40207:coef.interaction · Parada: answered

    Figura da resposta: call_31d40207:coef.interaction = -0,0834813-0.0834813334522015 (se 0,4171810.4171809476066827)
29. 28Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
30. 29Pontuação
    #1figura da resposta próximapassouobrigatória-0.08348133345 in [-0.9011559908, 0.7341933239]?
31. 30Pontuação
    #2figura da resposta próximapassouregistrada-0.08348133345 vs -0.08348133345223285
32. 31Pontuação
    #3figura da resposta próximapassouregistrada0.4171809476 vs 0.4171809476066692

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