Tarefa crb_papers.q048

Veredito

Pergunta feita ao analista

How did primary-school enrollment (percent of ages 5-14) evolve after the onset of a country's first civil war in 1880 compared with countries that had not experienced civil war by 1890?

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_0025— | Variable Name | Description | | --- | --- | | year | Gregorian calendar year of observation. One record per country-year. Coverage varies by country but spans much of 1828–2015. | | Countryid | Numeric identifier for countries in the panel. Serves as a stable code to group observations by country across years. | | primratio | Annual primary-school enrollment rate, expressed as a percentage of the resident population ages 5–14 enrolled in primary school (public and, where sources report it, private). Series are compiled from historical administrative counts and harmonized to a common 5–14 denominator; values are continuous percentages (0–100). | | treatment | Civil-war exposure indicator at the country-year level. Coded 1 from the first year of the earliest intrastate war that directly involved the state onward, and 0 otherwise; war timing is drawn from the Correlates of War intrastate war records. | Dataset Context: This is an annual country–year panel that brings together long-run measures of primary-school enrollment coverage and the timing of internal armed conflict for European and Latin American countries. The schooling series were constructed from historical administrative sources, anchored in International Historical Statistics and cross-checked against widely used decennial benchmarks, then extended backward with national statistical yearbooks, education reports compiled by the U.S. Bureau of Education (1872–1915), and regional data handbooks. The resulting enrollment measure reflects the share of children ages 5–14 enrolled in primary school each year and is available far earlier than most modern datasets, enabling analysis across the nineteenth, twentieth, and early twenty-first centuries. Civil conflict timing is drawn from the Correlates of War intrastate war dataset, focusing on episodes where the central state was a combatant. For each country, the panel flags the first qualifying episode and then maintains an “ever-exposed since onset” indicator thereafter, aligning conflict timing with the annual schooling series. Because the coverage begins as early as the 1820s for some countries and extends to 2015 for others, the panel spans periods of early state involvement in schooling, subsequent expansion and standardization of primary education, and the modern era of administrative reporting. Start years and continuity differ by country, reflecting when governments first recorded enrollment statistics and when national primary education systems took form. The dataset is suitable for historical description of how primary education coverage evolved over time in relation to episodes of internal conflict documented in comparative records.
   ref_adopt
   ref_panel
   ref_post
   ref_pre
   ref_years
   Transformação "select" aplicada à base "ref_adopt" (3016 → 3016 linhas). {"columns":["year","Countryid","primratio","treatment"]}
   Transformação "dropna" aplicada à base "ref_adopt" (3016 → 2882 linhas). {"columns":["year","Countryid","primratio","treatment"]}
   Transformação "filter" aplicada à base "ref_adopt" (2882 → 479 linhas). {"query":"treatment == 1"}
   Transformação "collapse" aplicada à base "ref_adopt" (479 → 24 linhas). {"aggs":[{"column":"year","fn":"min","name":"adopt"}],"by":["Countryid"]}
   Transformação "select" aplicada à base "ref_years" (3016 → 3016 linhas). {"columns":["year","Countryid","primratio","treatment"]}
   Transformação "dropna" aplicada à base "ref_years" (3016 → 2882 linhas). {"columns":["year","Countryid","primratio","treatment"]}
   Transformação "filter" aplicada à base "ref_years" (2882 → 28 linhas). {"query":"year in [1879, 1890]"}
   Transformação "collapse" aplicada à base "ref_years" (28 → 28 linhas). {"aggs":[{"column":"primratio","fn":"count","name":"n"}],"by":["Countryid","year"]}
   Transformação "collapse" aplicada à base "ref_years" (28 → 18 linhas). {"aggs":[{"column":"n","fn":"count","name":"nyears"}],"by":["Countryid"]}
   Transformação "select" aplicada à base "ref_pre" (3016 → 3016 linhas). {"columns":["year","Countryid","primratio","treatment"]}
   Transformação "dropna" aplicada à base "ref_pre" (3016 → 2882 linhas). {"columns":["year","Countryid","primratio","treatment"]}
   Transformação "filter" aplicada à base "ref_pre" (2882 → 10 linhas). {"query":"year == 1879"}
   Transformação "select" aplicada à base "ref_pre" (10 → 10 linhas). {"columns":["Countryid","treatment"]}
   Transformação "rename" aplicada à base "ref_pre" (10 → 10 linhas). {"map":{"treatment":"t_pre"}}
   Transformação "select" aplicada à base "ref_post" (3016 → 3016 linhas). {"columns":["year","Countryid","primratio","treatment"]}
   Transformação "dropna" aplicada à base "ref_post" (3016 → 2882 linhas). {"columns":["year","Countryid","primratio","treatment"]}
   Transformação "filter" aplicada à base "ref_post" (2882 → 18 linhas). {"query":"year == 1890"}
   Transformação "select" aplicada à base "ref_post" (18 → 18 linhas). {"columns":["Countryid","treatment"]}
   Transformação "rename" aplicada à base "ref_post" (18 → 18 linhas). {"map":{"treatment":"t_post"}}
   Transformação "select" aplicada à base "ref_panel" (3016 → 3016 linhas). {"columns":["year","Countryid","primratio","treatment"]}
   Transformação "dropna" aplicada à base "ref_panel" (3016 → 2882 linhas). {"columns":["year","Countryid","primratio","treatment"]}
   Transformação "filter" aplicada à base "ref_panel" (2882 → 28 linhas). {"query":"year in [1879, 1890]"}
   Transformação "merge" aplicada à base "ref_panel" (28 → 28 linhas). {"how":"left","on":["Countryid"],"with":"ref_years"}
   Transformação "merge" aplicada à base "ref_panel" (28 → 28 linhas). {"how":"left","on":["Countryid"],"with":"ref_adopt"}
   Transformação "merge" aplicada à base "ref_panel" (28 → 28 linhas). {"how":"left","on":["Countryid"],"with":"ref_pre"}
   Transformação "merge" aplicada à base "ref_panel" (28 → 28 linhas). {"how":"left","on":["Countryid"],"with":"ref_post"}
   Transformação "filter" aplicada à base "ref_panel" (28 → 20 linhas). {"query":"nyears == 2 and (adopt == 1880 or (t_pre == 0 and t_post == 0))"}
   Transformação "derive" aplicada à base "ref_panel" (20 → 20 linhas). {"args":{"cmp":"eq","column":"adopt","value":1880},"name":"TreatCohort","op":"indicator"}
   Transformação "derive" aplicada à base "ref_panel" (20 → 20 linhas). {"args":{"column":"TreatCohort","default":0,"keep_unmapped":false,"mapping":[{"source":1,"target":1}]},"name":"TreatCohort","op":"map"}
   Transformação "derive" aplicada à base "ref_panel" (20 → 20 linhas). {"args":{"cmp":"eq","column":"year","value":1890},"name":"Post","op":"indicator"}
   Transformação "derive" aplicada à base "ref_panel" (20 → 20 linhas). {"args":{"columns":["TreatCohort","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_436e9153 nos detalhes

   sm.OLS(primratio, add_constant([Post, TreatCohort, Interaction])).fit(cov_type='cluster', groups=Countryid) (CR1 errors with statsmodels' default G/(G-1)·(N-1)/(N-K) correction)

   Por que este é o caminho de referência: sm.OLS(primratio, add_constant([Post, TreatCohort, Interaction])).fit(cov_type='cluster', groups=Countryid) (CR1 errors with statsmodels' default G/(G-1)·(N-1)/(N-K) correction)
   Argumentos principais

   cluster:

   "Countryid"

   controls:

   ["Post","TreatCohort"]

   dataset:

   "ref_panel"

   intercept:

   true

   level:

   0,950.95

   se:

   "HC1"

   x:

   ["Interaction"]

   y:

   "primratio"

   Resultado calculado

   • coef.Interaction = 8,1777 8.177704705132376
     Coefficient on Interaction in the regression of primratio
   Diagnósticos: n_obs = 2020 (observations used) · k = 44 (parameters estimated, fixed-effect dummies included) · r2 = 0,5137190.5137192551666445 · df_resid = 1616 · se_type = "cluster" (cluster-robust standard errors (CR1) by 'Countryid') · n_clusters = 1010 (10 clusters of 'Countryid'; below about 40, cluster-robust errors are too small — confirm with infer.randomization_inference or a wild cluster bootstrap)
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: 8,178
   Figuras citadas: call_436e9153: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.q048.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

   How did primary-school enrollment (percent of ages 5-14) evolve after the onset of a country's first civil war in 1880 compared with countries that had not experienced civil war by 1890?

   ds_0025 (agent)

   | Variable Name | Description | | --- | --- | | year | Gregorian calendar year of observation. One record per country-year. Coverage varies by country but spans much of 1828–2015. | | Countryid | Numeric identifier for countries in the panel. Serves as a stable code to group observations by country across years. | | primratio | Annual primary-school enrollment rate, expressed as a percentage of the resident population ages 5–14 enrolled in primary school (public and, where sources report it, private). Series are compiled from historical administrative counts and harmonized to a common 5–14 denominator; values are continuous percentages (0–100). | | treatment | Civil-war exposure indicator at the country-year level. Coded 1 from the first year of the earliest intrastate war that directly involved the state onward, and 0 otherwise; war timing is drawn from the Correlates of War intrastate war records. | Dataset Context: This is an annual country–year panel that brings together long-run measures of primary-school enrollment coverage and the timing of internal armed conflict for European and Latin American countries. The schooling series were constructed from historical administrative sources, anchored in International Historical Statistics and cross-checked against widely used decennial benchmarks, then extended backward with national statistical yearbooks, education reports compiled by the U.S. Bureau of Education (1872–1915), and regional data handbooks. The resulting enrollment measure reflects the share of children ages 5–14 enrolled in primary school each year and is available far earlier than most modern datasets, enabling analysis across the nineteenth, twentieth, and early twenty-first centuries. Civil conflict timing is drawn from the Correlates of War intrastate war dataset, focusing on episodes where the central state was a combatant. For each country, the panel flags the first qualifying episode and then maintains an “ever-exposed since onset” indicator thereafter, aligning conflict timing with the annual schooling series. Because the coverage begins as early as the 1820s for some countries and extends to 2015 for others, the panel spans periods of early state involvement in schooling, subsequent expansion and standardization of primary education, and the modern era of administrative reporting. Start years and continuity differ by country, reflecting when governments first recorded enrollment statistics and when national primary education systems took form. The dataset is suitable for historical description of how primary education coverage evolved over time in relation to episodes of internal conflict documented in comparative records.

   file c4a713c165661449eafadc60e8646b778b01b8c8bff567f77bc6adaa39ba2fb0 · frame —

   ref_adopt (reference)

   file c4a713c165661449eafadc60e8646b778b01b8c8bff567f77bc6adaa39ba2fb0 · frame —

   1. select {"columns":["year","Countryid","primratio","treatment"]}
   2. dropna {"columns":["year","Countryid","primratio","treatment"]}
   3. filter {"query":"treatment == 1"}
   4. collapse {"aggs":[{"column":"year","fn":"min","name":"adopt"}],"by":["Countryid"]}

   ref_panel (reference)

   file c4a713c165661449eafadc60e8646b778b01b8c8bff567f77bc6adaa39ba2fb0 · frame —

   1. select {"columns":["year","Countryid","primratio","treatment"]}
   2. dropna {"columns":["year","Countryid","primratio","treatment"]}
   3. filter {"query":"year in [1879, 1890]"}
   4. merge {"how":"left","on":["Countryid"],"with":"ref_years"}
   5. merge {"how":"left","on":["Countryid"],"with":"ref_adopt"}
   6. merge {"how":"left","on":["Countryid"],"with":"ref_pre"}
   7. merge {"how":"left","on":["Countryid"],"with":"ref_post"}
   8. filter {"query":"nyears == 2 and (adopt == 1880 or (t_pre == 0 and t_post == 0))"}
   9. derive {"args":{"cmp":"eq","column":"adopt","value":1880},"name":"TreatCohort","op":"indicator"}
   10. derive {"args":{"column":"TreatCohort","default":0,"keep_unmapped":false,"mapping":[{"source":1,"target":1}]},"name":"TreatCohort","op":"map"}
   11. derive {"args":{"cmp":"eq","column":"year","value":1890},"name":"Post","op":"indicator"}
   12. derive {"args":{"columns":["TreatCohort","Post"]},"name":"Interaction","op":"interaction"}

   ref_post (reference)

   file c4a713c165661449eafadc60e8646b778b01b8c8bff567f77bc6adaa39ba2fb0 · frame —

   1. select {"columns":["year","Countryid","primratio","treatment"]}
   2. dropna {"columns":["year","Countryid","primratio","treatment"]}
   3. filter {"query":"year == 1890"}
   4. select {"columns":["Countryid","treatment"]}
   5. rename {"map":{"treatment":"t_post"}}

   ref_pre (reference)

   file c4a713c165661449eafadc60e8646b778b01b8c8bff567f77bc6adaa39ba2fb0 · frame —

   1. select {"columns":["year","Countryid","primratio","treatment"]}
   2. dropna {"columns":["year","Countryid","primratio","treatment"]}
   3. filter {"query":"year == 1879"}
   4. select {"columns":["Countryid","treatment"]}
   5. rename {"map":{"treatment":"t_pre"}}

   ref_years (reference)

   file c4a713c165661449eafadc60e8646b778b01b8c8bff567f77bc6adaa39ba2fb0 · frame —

   1. select {"columns":["year","Countryid","primratio","treatment"]}
   2. dropna {"columns":["year","Countryid","primratio","treatment"]}
   3. filter {"query":"year in [1879, 1890]"}
   4. collapse {"aggs":[{"column":"primratio","fn":"count","name":"n"}],"by":["Countryid","year"]}
   5. collapse {"aggs":[{"column":"n","fn":"count","name":"nyears"}],"by":["Countryid"]}
2. 1Transformação
   ref_adopt · select {"columns":["year","Countryid","primratio","treatment"]}

   3016 → 3016 linhas

   Frame antes → depois: d6c08e9f9b08 → d6c08e9f9b08
3. 2Transformação
   ref_adopt · dropna {"columns":["year","Countryid","primratio","treatment"]}

   3016 → 2882 linhas

   Frame antes → depois: d6c08e9f9b08 → 725c68f5e100
4. 3Transformação
   ref_adopt · filter {"query":"treatment == 1"}

   2882 → 479 linhas

   Frame antes → depois: 725c68f5e100 → ec3f80967a77
5. 4Transformação
   ref_adopt · collapse {"aggs":[{"column":"year","fn":"min","name":"adopt"}],"by":["Countryid"]}

   479 → 24 linhas

   Frame antes → depois: ec3f80967a77 → 3d091c3870d8
6. 5Transformação
   ref_years · select {"columns":["year","Countryid","primratio","treatment"]}

   3016 → 3016 linhas

   Frame antes → depois: d6c08e9f9b08 → d6c08e9f9b08
7. 6Transformação
   ref_years · dropna {"columns":["year","Countryid","primratio","treatment"]}

   3016 → 2882 linhas

   Frame antes → depois: d6c08e9f9b08 → 725c68f5e100
8. 7Transformação
   ref_years · filter {"query":"year in [1879, 1890]"}

   2882 → 28 linhas

   Frame antes → depois: 725c68f5e100 → e6d4b31bc53a
9. 8Transformação
   ref_years · collapse {"aggs":[{"column":"primratio","fn":"count","name":"n"}],"by":["Countryid","year"]}

   28 → 28 linhas

   Frame antes → depois: e6d4b31bc53a → 2da1bdc6ec9e
10. 9Transformação
    ref_years · collapse {"aggs":[{"column":"n","fn":"count","name":"nyears"}],"by":["Countryid"]}

    28 → 18 linhas

    Frame antes → depois: 2da1bdc6ec9e → fb7409edc28d
11. 10Transformação
    ref_pre · select {"columns":["year","Countryid","primratio","treatment"]}

    3016 → 3016 linhas

    Frame antes → depois: d6c08e9f9b08 → d6c08e9f9b08
12. 11Transformação
    ref_pre · dropna {"columns":["year","Countryid","primratio","treatment"]}

    3016 → 2882 linhas

    Frame antes → depois: d6c08e9f9b08 → 725c68f5e100
13. 12Transformação
    ref_pre · filter {"query":"year == 1879"}

    2882 → 10 linhas

    Frame antes → depois: 725c68f5e100 → 0456e1e2202b
14. 13Transformação
    ref_pre · select {"columns":["Countryid","treatment"]}

    10 → 10 linhas

    Frame antes → depois: 0456e1e2202b → 4c03374f159a
15. 14Transformação
    ref_pre · rename {"map":{"treatment":"t_pre"}}

    10 → 10 linhas

    Frame antes → depois: 4c03374f159a → 7d049f36c155
16. 15Transformação
    ref_post · select {"columns":["year","Countryid","primratio","treatment"]}

    3016 → 3016 linhas

    Frame antes → depois: d6c08e9f9b08 → d6c08e9f9b08
17. 16Transformação
    ref_post · dropna {"columns":["year","Countryid","primratio","treatment"]}

    3016 → 2882 linhas

    Frame antes → depois: d6c08e9f9b08 → 725c68f5e100
18. 17Transformação
    ref_post · filter {"query":"year == 1890"}

    2882 → 18 linhas

    Frame antes → depois: 725c68f5e100 → 44eea2971946
19. 18Transformação
    ref_post · select {"columns":["Countryid","treatment"]}

    18 → 18 linhas

    Frame antes → depois: 44eea2971946 → 4bf9ce1a15d6
20. 19Transformação
    ref_post · rename {"map":{"treatment":"t_post"}}

    18 → 18 linhas

    Frame antes → depois: 4bf9ce1a15d6 → 521ef1baedd3
21. 20Transformação
    ref_panel · select {"columns":["year","Countryid","primratio","treatment"]}

    3016 → 3016 linhas

    Frame antes → depois: d6c08e9f9b08 → d6c08e9f9b08
22. 21Transformação
    ref_panel · dropna {"columns":["year","Countryid","primratio","treatment"]}

    3016 → 2882 linhas

    Frame antes → depois: d6c08e9f9b08 → 725c68f5e100
23. 22Transformação
    ref_panel · filter {"query":"year in [1879, 1890]"}

    2882 → 28 linhas

    Frame antes → depois: 725c68f5e100 → e6d4b31bc53a
24. 23Transformação
    ref_panel · merge {"how":"left","on":["Countryid"],"with":"ref_years"}

    28 → 28 linhas

    Frame antes → depois: e6d4b31bc53a → 583102c04442
25. 24Transformação
    ref_panel · merge {"how":"left","on":["Countryid"],"with":"ref_adopt"}

    28 → 28 linhas

    Frame antes → depois: 583102c04442 → cf9248d196c0
26. 25Transformação
    ref_panel · merge {"how":"left","on":["Countryid"],"with":"ref_pre"}

    28 → 28 linhas

    Frame antes → depois: cf9248d196c0 → 78b77811812c
27. 26Transformação
    ref_panel · merge {"how":"left","on":["Countryid"],"with":"ref_post"}

    28 → 28 linhas

    Frame antes → depois: 78b77811812c → 5c8dd6b4ab15
28. 27Transformação
    ref_panel · filter {"query":"nyears == 2 and (adopt == 1880 or (t_pre == 0 and t_post == 0))"}

    28 → 20 linhas

    Frame antes → depois: 5c8dd6b4ab15 → b7f82d45cdd1
29. 28Transformação
    ref_panel · derive {"args":{"cmp":"eq","column":"adopt","value":1880},"name":"TreatCohort","op":"indicator"}

    20 → 20 linhas

    Frame antes → depois: b7f82d45cdd1 → d9d3fba3d2c8
30. 29Transformação
    ref_panel · derive {"args":{"column":"TreatCohort","default":0,"keep_unmapped":false,"mapping":[{"source":1,"target":1}]},"name":"TreatCohort","op":"map"}

    20 → 20 linhas

    Frame antes → depois: d9d3fba3d2c8 → 273ca57846bc
31. 30Transformação
    ref_panel · derive {"args":{"cmp":"eq","column":"year","value":1890},"name":"Post","op":"indicator"}

    20 → 20 linhas

    Frame antes → depois: 273ca57846bc → 1d94d7a7a10f
32. 31Transformação
    ref_panel · derive {"args":{"columns":["TreatCohort","Post"]},"name":"Interaction","op":"interaction"}

    20 → 20 linhas

    Frame antes → depois: 1d94d7a7a10f → 1f5519a88885
33. 32Turno do modelo
    call_d551b3ecreferenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

    Texto visível

    sm.OLS(primratio, add_constant([Post, TreatCohort, Interaction])).fit(cov_type='cluster', groups=Countryid) (CR1 errors with statsmodels' default G/(G-1)·(N-1)/(N-K) correction)

    Chamadas pedidas

    • infer.ols

      {
        "cluster": "Countryid",
        "controls": [
          "Post",
          "TreatCohort"
        ],
        "dataset": "ref_panel",
        "x": [
          "Interaction"
        ],
        "y": "primratio"
      }

    Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
34. 33Ferramenta
    infer.ols— Regressão linear por mínimos quadrados ordinários (MQO)v0.1.0call_436e9153decidido pelo modeloreexecutável

    Motivo: sm.OLS(primratio, add_constant([Post, TreatCohort, Interaction])).fit(cov_type='cluster', groups=Countryid) (CR1 errors with statsmodels' default G/(G-1)·(N-1)/(N-K) correction)

    Argumentos

    {
      "cluster": "Countryid",
      "controls": [
        "Post",
        "TreatCohort"
      ],
      "dataset": "ref_panel",
      "fe": [],
      "intercept": true,
      "level": 0.95,
      "se": "HC1",
      "subset": null,
      "weights": null,
      "x": [
        "Interaction"
      ],
      "y": "primratio"
    }

    Figuras call_436e9153

    Chave	Valor	Valor bruto	Erro-padrão	IC
    coef.Interaction Coefficient on Interaction in the regression of primratio	8,1777	8.177704705132376	2,758462.7584557435386787	1,93764 – 14,4178

    Diagnósticos

    • n_obs = 2020 (observations used)
    • k = 44 (parameters estimated, fixed-effect dummies included)
    • r2 = 0,5137190.5137192551666445
    • df_resid = 1616
    • se_type = "cluster" (cluster-robust standard errors (CR1) by 'Countryid')
    • n_clusters = 1010 (10 clusters of 'Countryid'; below about 40, cluster-robust errors are too small — confirm with infer.randomization_inference or a wild cluster bootstrap)

    Derivação matemática e pressupostos

    coef.Interaction

    1. Coeficiente de Interaction na regressão por mínimos quadrados ordinários de primratio.→ 8,1777 (8.177704705132376)
    2. Erro-padrão robusto por conglomerado (CR1): a raiz da variância estimada do coeficiente.ep = √V_jj→ 2,75846 (2.7584557435386787)
    3. Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 9 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 'Countryid' and may be correlated within them

    Tabelas (1)

    {
      "coefficients": [
        {
          "ci_high": 70.24263660217018,
          "ci_low": 47.69250930576168,
          "coef": 58.96757295396593,
          "p": 8.68866354858933e-7,
          "se": 4.984208804598444,
          "t": 11.830879336267433,
          "term": "Intercept"
        },
        {
          "ci_high": 14.417765123640246,
          "ci_low": 1.937644286624506,
          "coef": 8.177704705132376,
          "p": 0.01584084594675597,
          "se": 2.7584557435386787,
          "t": 2.9645952175551766,
          "term": "Interaction"
        },
        {
          "ci_high": 8.742375092037602,
          "ci_low": -3.7377457449781333,
          "coef": 2.502314673529734,
          "p": 0.3879740939881394,
          "se": 2.7584557435386774,
          "t": 0.9071433099447325,
          "term": "Post"
        },
        {
          "ci_high": -29.922909391210897,
          "ci_low": -52.4730366876194,
          "coef": -41.197973039415146,
          "p": 0.000017033530254617854,
          "se": 4.9842088045984445,
          "t": -8.265699663586682,
          "term": "TreatCohort"
        }
      ]
    }

    Hashes SHA-256 e metadados de reprodução

    Digests dos dados

    ref_panel 1f5519a88885ddc77a32ad1e01af3e32f2028b5777fd9f42259f4d049b4efddb

    sha256 do resultado

    af04cd8190eb0d5de22ef8f1e70c9b335de19a7e12b9333894b0eafacaa4961f

    Resultado mostrado ao modelo
35. 34Turno do modelo
    call_1413097freferenceFim: 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 = 8,178. Reference path transcribed from the item's own estimation script.",
        "caveats": [],
        "figure_refs": [
          "call_436e9153:coef.Interaction"
        ]
      }

    Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
36. 35Guarda
    resposta aceita

    Números encontrados

    8,178

    Sem lastro

    —

    Não citados

    —

    1 nova(s) tentativa(s) restante(s)
37. 36Resposta

    Estimativa (infer.ols): coef.Interaction = 8,178. Reference path transcribed from the item's own estimation script.

    Figuras citadas: call_436e9153:coef.Interaction · Parada: answered

    Figura da resposta: call_436e9153:coef.Interaction = 8,17778.177704705132376 (se 2,758462.7584557435386787)
38. 37Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
39. 38Pontuação
    #1figura da resposta próximapassouobrigatória8.177704705 in [2.771131448, 13.58427796]?
40. 39Pontuação
    #2figura da resposta próximapassouregistrada8.177704705 vs 8.177704705132356
41. 40Pontuação
    #3figura da resposta próximapassouregistrada2.758455744 vs 2.7584557435386707

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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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