Tarefa crb_papers.q045

Veredito

Pergunta feita ao analista

Among favela complexes where a UPP first became active at month index 606, how did the average monthly rate of fatal police shootings (Autos de Resistencia) per 100,000 residents change from month indices 594–605 to 606–617, relative to complexes that remained without an active UPP through 617, accounting for BOPE or armed-forces occupation?

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_0023— | Variable Name | Description | | --- | --- | | complexo_id | Stable numeric identifier for each favela complex (a cluster of adjacent favelas) used as the unit of observation across months. | | upp_post | Binary indicator equal to 1 in months when a Pacifying Police Unit is formally active in the complexo, and 0 otherwise. Reflects the period of permanent community-policing presence established by the state. | | bope | Binary indicator equal to 1 in months when the Special Police Operations Battalion (BOPE) or the armed forces maintained an occupation/stabilization presence in the complexo, and 0 otherwise. In the administrative records used here, these occupation periods are coded separately from the permanent posts. | | date | Integer month index running from 540 through 677 in this extract; an increase of 1 denotes one month. Use these recorded indices to select periods; no calendar-date conversion is specified here. | | autos_rate2 | Monthly rate per 100,000 residents of deaths officially registered as “Autos de Resistência” (fatalities during police operations) within the complexo. Incidents were geocoded to complexo boundaries and aggregated by month; rates use population estimates as denominators. | | complexo2 | Auxiliary numeric field carried over from the source export related to the complexo dimension; no substantive documentation provided in the materials. | Dataset Context: This panel assembles monthly records for favela complexes in Rio de Janeiro during a period when the state created permanent policing posts in selected territories. It integrates two administrative sources: (1) public security records on deaths registered during police operations (locally termed “Autos de Resistência” in the period covered) and (2) program logs detailing when permanent posts were active and when occupation/stabilization forces (BOPE or the armed forces) were present. Security incidents were geocoded to mapped complex boundaries, aggregated to the complex–month level, and converted to rates per 100,000 using census-based population estimates for each complex. The spatial unit—a complexo—groups adjacent favelas that share borders and, in practice, correspond to the operational footprint of policing deployments. The temporal resolution is monthly, spanning indices 540–677, a phase that includes the roll‑out and consolidation of permanent posts and earlier occupation phases. In the coding scheme used here, occupation/stabilization months and permanent‑post months are tracked with nonoverlapping indicators. For the comparison centered on first UPP activation at index 589, the earlier window is 577–588 and the later window is 589–600; comparison complexes remain without an active UPP through 600. For first activation at 606, the windows are 594–605 and 606–617, with comparison complexes remaining without an active UPP through 617. Each window contains 12 months. The outcome for each complex and window is its mean monthly fatal-shooting rate, not a cumulative count; the occupation indicator is likewise averaged within each window. Measurement follows local administrative definitions in effect at the time. “Autos de Resistência” reflects fatalities recorded as resulting from confrontations with police; these events were manually geocoded and then aggregated to complex–month cells. Rates normalize counts by resident population to facilitate comparisons across differently sized complexes and over time. As with any administrative system, users should be mindful of potential changes in reporting practices, geocoding precision, and population denominators when interpreting temporal or cross‑sectional variation.
   ref_panel
   ref_post
   ref_pre
   ref_td
   Transformação "select" aplicada à base "ref_td" (36956 → 36956 linhas). {"columns":["complexo_id","upp_post","bope","date","autos_rate2"]}
   Transformação "dropna" aplicada à base "ref_td" (36956 → 36956 linhas). {"columns":["autos_rate2"]}
   Transformação "filter" aplicada à base "ref_td" (36956 → 2127 linhas). {"query":"upp_post == 1"}
   Transformação "collapse" aplicada à base "ref_td" (2127 → 37 linhas). {"aggs":[{"column":"date","fn":"min","name":"td"}],"by":["complexo_id"]}
   Transformação "select" aplicada à base "ref_pre" (36956 → 36956 linhas). {"columns":["complexo_id","upp_post","bope","date","autos_rate2"]}
   Transformação "dropna" aplicada à base "ref_pre" (36956 → 36956 linhas). {"columns":["autos_rate2"]}
   Transformação "filter" aplicada à base "ref_pre" (36956 → 3420 linhas). {"query":"date >= 594 and date <= 605"}
   Transformação "collapse" aplicada à base "ref_pre" (3420 → 285 linhas). {"aggs":[{"column":"autos_rate2","fn":"mean","name":"y0"},{"column":"bope","fn":"mean","name":"bope0"}],"by":["complexo_id"]}
   Transformação "select" aplicada à base "ref_post" (36956 → 36956 linhas). {"columns":["complexo_id","upp_post","bope","date","autos_rate2"]}
   Transformação "dropna" aplicada à base "ref_post" (36956 → 36956 linhas). {"columns":["autos_rate2"]}
   Transformação "filter" aplicada à base "ref_post" (36956 → 3420 linhas). {"query":"date >= 606 and date <= 617"}
   Transformação "collapse" aplicada à base "ref_post" (3420 → 285 linhas). {"aggs":[{"column":"autos_rate2","fn":"mean","name":"y1"},{"column":"bope","fn":"mean","name":"bope1"}],"by":["complexo_id"]}
   Transformação "select" aplicada à base "ref_panel" (36956 → 36956 linhas). {"columns":["complexo_id","upp_post","bope","date","autos_rate2"]}
   Transformação "dropna" aplicada à base "ref_panel" (36956 → 36956 linhas). {"columns":["autos_rate2"]}
   Transformação "collapse" aplicada à base "ref_panel" (36956 → 286 linhas). {"aggs":[{"column":"autos_rate2","fn":"count","name":"n"}],"by":["complexo_id"]}
   Transformação "select" aplicada à base "ref_panel" (286 → 286 linhas). {"columns":["complexo_id"]}
   Transformação "merge" aplicada à base "ref_panel" (286 → 286 linhas). {"how":"left","on":["complexo_id"],"with":"ref_td"}
   Transformação "filter" aplicada à base "ref_panel" (286 → 272 linhas). {"query":"td == 606 or td != td or td > 617"}
   Transformação "merge" aplicada à base "ref_panel" (272 → 271 linhas). {"how":"inner","on":["complexo_id"],"with":"ref_pre"}
   Transformação "merge" aplicada à base "ref_panel" (271 → 271 linhas). {"how":"inner","on":["complexo_id"],"with":"ref_post"}
   Transformação "derive" aplicada à base "ref_panel" (271 → 271 linhas). {"args":{"cmp":"eq","column":"td","value":606},"name":"treated","op":"indicator"}
   Transformação "derive" aplicada à base "ref_panel" (271 → 271 linhas). {"args":{"column":"treated","default":0,"keep_unmapped":false,"mapping":[{"source":1,"target":1}]},"name":"treated","op":"map"}
   Transformação "reshape_long" aplicada à base "ref_panel" (271 → 542 linhas). {"id":["complexo_id"],"j":"post","stubs":["y","bope"]}
   Transformação "derive" aplicada à base "ref_panel" (542 → 542 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_526ed370 nos detalhes

   sm.OLS(y, [const, treated, post, interaction, bope]).fit(cov_type='cluster', groups=unit, use_t=True) (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, interaction, bope]).fit(cov_type='cluster', groups=unit, use_t=True) (CR1 errors with statsmodels' default G/(G-1)·(N-1)/(N-K) correction)
   Argumentos principais

   cluster:

   "complexo_id"

   controls:

   ["treated","post","bope"]

   dataset:

   "ref_panel"

   intercept:

   true

   level:

   0,950.95

   se:

   "HC1"

   x:

   ["interaction"]

   y:

   "y"

   Resultado calculado

   • coef.interaction = -2,17407 -2.1740686776929077
     Coefficient on interaction in the regression of y
   Diagnósticos: n_obs = 542542 (observations used) · k = 55 (parameters estimated, fixed-effect dummies included) · r2 = 0,00155030.0015502966885545533 · df_resid = 537537 · se_type = "cluster" (cluster-robust standard errors (CR1) by 'complexo_id') · n_clusters = 271271 (271 clusters of 'complexo_id')
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: -2,174
   Figuras citadas: call_526ed370: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.q045.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 favela complexes where a UPP first became active at month index 606, how did the average monthly rate of fatal police shootings (Autos de Resistencia) per 100,000 residents change from month indices 594–605 to 606–617, relative to complexes that remained without an active UPP through 617, accounting for BOPE or armed-forces occupation?

   ds_0023 (agent)

   | Variable Name | Description | | --- | --- | | complexo_id | Stable numeric identifier for each favela complex (a cluster of adjacent favelas) used as the unit of observation across months. | | upp_post | Binary indicator equal to 1 in months when a Pacifying Police Unit is formally active in the complexo, and 0 otherwise. Reflects the period of permanent community-policing presence established by the state. | | bope | Binary indicator equal to 1 in months when the Special Police Operations Battalion (BOPE) or the armed forces maintained an occupation/stabilization presence in the complexo, and 0 otherwise. In the administrative records used here, these occupation periods are coded separately from the permanent posts. | | date | Integer month index running from 540 through 677 in this extract; an increase of 1 denotes one month. Use these recorded indices to select periods; no calendar-date conversion is specified here. | | autos_rate2 | Monthly rate per 100,000 residents of deaths officially registered as “Autos de Resistência” (fatalities during police operations) within the complexo. Incidents were geocoded to complexo boundaries and aggregated by month; rates use population estimates as denominators. | | complexo2 | Auxiliary numeric field carried over from the source export related to the complexo dimension; no substantive documentation provided in the materials. | Dataset Context: This panel assembles monthly records for favela complexes in Rio de Janeiro during a period when the state created permanent policing posts in selected territories. It integrates two administrative sources: (1) public security records on deaths registered during police operations (locally termed “Autos de Resistência” in the period covered) and (2) program logs detailing when permanent posts were active and when occupation/stabilization forces (BOPE or the armed forces) were present. Security incidents were geocoded to mapped complex boundaries, aggregated to the complex–month level, and converted to rates per 100,000 using census-based population estimates for each complex. The spatial unit—a complexo—groups adjacent favelas that share borders and, in practice, correspond to the operational footprint of policing deployments. The temporal resolution is monthly, spanning indices 540–677, a phase that includes the roll‑out and consolidation of permanent posts and earlier occupation phases. In the coding scheme used here, occupation/stabilization months and permanent‑post months are tracked with nonoverlapping indicators. For the comparison centered on first UPP activation at index 589, the earlier window is 577–588 and the later window is 589–600; comparison complexes remain without an active UPP through 600. For first activation at 606, the windows are 594–605 and 606–617, with comparison complexes remaining without an active UPP through 617. Each window contains 12 months. The outcome for each complex and window is its mean monthly fatal-shooting rate, not a cumulative count; the occupation indicator is likewise averaged within each window. Measurement follows local administrative definitions in effect at the time. “Autos de Resistência” reflects fatalities recorded as resulting from confrontations with police; these events were manually geocoded and then aggregated to complex–month cells. Rates normalize counts by resident population to facilitate comparisons across differently sized complexes and over time. As with any administrative system, users should be mindful of potential changes in reporting practices, geocoding precision, and population denominators when interpreting temporal or cross‑sectional variation.

   file 713df5a475f4ed9dbc7d9c5c5ae48ca0baaa990b25b56e1cf40b8c97ae7d78ad · frame —

   ref_panel (reference)

   file 713df5a475f4ed9dbc7d9c5c5ae48ca0baaa990b25b56e1cf40b8c97ae7d78ad · frame —

   1. select {"columns":["complexo_id","upp_post","bope","date","autos_rate2"]}
   2. dropna {"columns":["autos_rate2"]}
   3. collapse {"aggs":[{"column":"autos_rate2","fn":"count","name":"n"}],"by":["complexo_id"]}
   4. select {"columns":["complexo_id"]}
   5. merge {"how":"left","on":["complexo_id"],"with":"ref_td"}
   6. filter {"query":"td == 606 or td != td or td > 617"}
   7. merge {"how":"inner","on":["complexo_id"],"with":"ref_pre"}
   8. merge {"how":"inner","on":["complexo_id"],"with":"ref_post"}
   9. derive {"args":{"cmp":"eq","column":"td","value":606},"name":"treated","op":"indicator"}
   10. derive {"args":{"column":"treated","default":0,"keep_unmapped":false,"mapping":[{"source":1,"target":1}]},"name":"treated","op":"map"}
   11. reshape_long {"id":["complexo_id"],"j":"post","stubs":["y","bope"]}
   12. derive {"args":{"columns":["treated","post"]},"name":"interaction","op":"interaction"}

   ref_post (reference)

   file 713df5a475f4ed9dbc7d9c5c5ae48ca0baaa990b25b56e1cf40b8c97ae7d78ad · frame —

   1. select {"columns":["complexo_id","upp_post","bope","date","autos_rate2"]}
   2. dropna {"columns":["autos_rate2"]}
   3. filter {"query":"date >= 606 and date <= 617"}
   4. collapse {"aggs":[{"column":"autos_rate2","fn":"mean","name":"y1"},{"column":"bope","fn":"mean","name":"bope1"}],"by":["complexo_id"]}

   ref_pre (reference)

   file 713df5a475f4ed9dbc7d9c5c5ae48ca0baaa990b25b56e1cf40b8c97ae7d78ad · frame —

   1. select {"columns":["complexo_id","upp_post","bope","date","autos_rate2"]}
   2. dropna {"columns":["autos_rate2"]}
   3. filter {"query":"date >= 594 and date <= 605"}
   4. collapse {"aggs":[{"column":"autos_rate2","fn":"mean","name":"y0"},{"column":"bope","fn":"mean","name":"bope0"}],"by":["complexo_id"]}

   ref_td (reference)

   file 713df5a475f4ed9dbc7d9c5c5ae48ca0baaa990b25b56e1cf40b8c97ae7d78ad · frame —

   1. select {"columns":["complexo_id","upp_post","bope","date","autos_rate2"]}
   2. dropna {"columns":["autos_rate2"]}
   3. filter {"query":"upp_post == 1"}
   4. collapse {"aggs":[{"column":"date","fn":"min","name":"td"}],"by":["complexo_id"]}
2. 1Transformação
   ref_td · select {"columns":["complexo_id","upp_post","bope","date","autos_rate2"]}

   36956 → 36956 linhas

   Frame antes → depois: a5a16e216dcd → 3b1627f1ee66
3. 2Transformação
   ref_td · dropna {"columns":["autos_rate2"]}

   36956 → 36956 linhas

   Frame antes → depois: 3b1627f1ee66 → 3b1627f1ee66
4. 3Transformação
   ref_td · filter {"query":"upp_post == 1"}

   36956 → 2127 linhas

   Frame antes → depois: 3b1627f1ee66 → 4d2583acd8a2
5. 4Transformação
   ref_td · collapse {"aggs":[{"column":"date","fn":"min","name":"td"}],"by":["complexo_id"]}

   2127 → 37 linhas

   Frame antes → depois: 4d2583acd8a2 → 1ccb5551ea29
6. 5Transformação
   ref_pre · select {"columns":["complexo_id","upp_post","bope","date","autos_rate2"]}

   36956 → 36956 linhas

   Frame antes → depois: a5a16e216dcd → 3b1627f1ee66
7. 6Transformação
   ref_pre · dropna {"columns":["autos_rate2"]}

   36956 → 36956 linhas

   Frame antes → depois: 3b1627f1ee66 → 3b1627f1ee66
8. 7Transformação
   ref_pre · filter {"query":"date >= 594 and date <= 605"}

   36956 → 3420 linhas

   Frame antes → depois: 3b1627f1ee66 → 3d8402cee8e4
9. 8Transformação
   ref_pre · collapse {"aggs":[{"column":"autos_rate2","fn":"mean","name":"y0"},{"column":"bope","fn":"mean","name":"bope0"}],"by":["complexo_id"]}

   3420 → 285 linhas

   Frame antes → depois: 3d8402cee8e4 → b31636c80119
10. 9Transformação
    ref_post · select {"columns":["complexo_id","upp_post","bope","date","autos_rate2"]}

    36956 → 36956 linhas

    Frame antes → depois: a5a16e216dcd → 3b1627f1ee66
11. 10Transformação
    ref_post · dropna {"columns":["autos_rate2"]}

    36956 → 36956 linhas

    Frame antes → depois: 3b1627f1ee66 → 3b1627f1ee66
12. 11Transformação
    ref_post · filter {"query":"date >= 606 and date <= 617"}

    36956 → 3420 linhas

    Frame antes → depois: 3b1627f1ee66 → e510be771875
13. 12Transformação
    ref_post · collapse {"aggs":[{"column":"autos_rate2","fn":"mean","name":"y1"},{"column":"bope","fn":"mean","name":"bope1"}],"by":["complexo_id"]}

    3420 → 285 linhas

    Frame antes → depois: e510be771875 → 90a230e2dc74
14. 13Transformação
    ref_panel · select {"columns":["complexo_id","upp_post","bope","date","autos_rate2"]}

    36956 → 36956 linhas

    Frame antes → depois: a5a16e216dcd → 3b1627f1ee66
15. 14Transformação
    ref_panel · dropna {"columns":["autos_rate2"]}

    36956 → 36956 linhas

    Frame antes → depois: 3b1627f1ee66 → 3b1627f1ee66
16. 15Transformação
    ref_panel · collapse {"aggs":[{"column":"autos_rate2","fn":"count","name":"n"}],"by":["complexo_id"]}

    36956 → 286 linhas

    Frame antes → depois: 3b1627f1ee66 → 301a2801ada7
17. 16Transformação
    ref_panel · select {"columns":["complexo_id"]}

    286 → 286 linhas

    Frame antes → depois: 301a2801ada7 → 137accc08167
18. 17Transformação
    ref_panel · merge {"how":"left","on":["complexo_id"],"with":"ref_td"}

    286 → 286 linhas

    Frame antes → depois: 137accc08167 → 2cd0217c33eb
19. 18Transformação
    ref_panel · filter {"query":"td == 606 or td != td or td > 617"}

    286 → 272 linhas

    Frame antes → depois: 2cd0217c33eb → 1859c4d25359
20. 19Transformação
    ref_panel · merge {"how":"inner","on":["complexo_id"],"with":"ref_pre"}

    272 → 271 linhas

    Frame antes → depois: 1859c4d25359 → b0c994396d24
21. 20Transformação
    ref_panel · merge {"how":"inner","on":["complexo_id"],"with":"ref_post"}

    271 → 271 linhas

    Frame antes → depois: b0c994396d24 → 74f3e1da70aa
22. 21Transformação
    ref_panel · derive {"args":{"cmp":"eq","column":"td","value":606},"name":"treated","op":"indicator"}

    271 → 271 linhas

    Frame antes → depois: 74f3e1da70aa → cee8b2e1635d
23. 22Transformação
    ref_panel · derive {"args":{"column":"treated","default":0,"keep_unmapped":false,"mapping":[{"source":1,"target":1}]},"name":"treated","op":"map"}

    271 → 271 linhas

    Frame antes → depois: cee8b2e1635d → 3c27cd326a9a
24. 23Transformação
    ref_panel · reshape_long {"id":["complexo_id"],"j":"post","stubs":["y","bope"]}

    271 → 542 linhas

    Frame antes → depois: 3c27cd326a9a → a05d629f2aef
25. 24Transformação
    ref_panel · derive {"args":{"columns":["treated","post"]},"name":"interaction","op":"interaction"}

    542 → 542 linhas

    Frame antes → depois: a05d629f2aef → 08a9abb05cbf
26. 25Turno do modelo
    call_79383644referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

    Texto visível

    sm.OLS(y, [const, treated, post, interaction, bope]).fit(cov_type='cluster', groups=unit, use_t=True) (CR1 errors with statsmodels' default G/(G-1)·(N-1)/(N-K) correction)

    Chamadas pedidas

    • infer.ols

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

    Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
27. 26Ferramenta
    infer.ols— Regressão linear por mínimos quadrados ordinários (MQO)v0.1.0call_526ed370decidido pelo modeloreexecutável

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

    Argumentos

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

    Figuras call_526ed370

    Chave	Valor	Valor bruto	Erro-padrão	IC
    coef.interaction Coefficient on interaction in the regression of y	-2,17407	-2.1740686776929077	0,4171780.41717801834785523	-2,9954 – -1,35273

    Diagnósticos

    • n_obs = 542542 (observations used)
    • k = 55 (parameters estimated, fixed-effect dummies included)
    • r2 = 0,00155030.0015502966885545533
    • df_resid = 537537
    • se_type = "cluster" (cluster-robust standard errors (CR1) by 'complexo_id')
    • n_clusters = 271271 (271 clusters of 'complexo_id')

    Derivação matemática e pressupostos

    coef.interaction

    1. Coeficiente de interaction na regressão por mínimos quadrados ordinários de y.→ -2,17407 (-2.1740686776929077)
    2. Erro-padrão robusto por conglomerado (CR1): a raiz da variância estimada do coeficiente.ep = √V_jj→ 0,417178 (0.41717801834785523)
    3. Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 270 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 'complexo_id' and may be correlated within them

    Tabelas (1)

    {
      "coefficients": [
        {
          "ci_high": 1.9859817759605338,
          "ci_low": 0.9141499337746558,
          "coef": 1.4500658548675949,
          "p": 2.1052321435964518e-7,
          "se": 0.27220586622005927,
          "t": 5.3270925972451995,
          "term": "Intercept"
        },
        {
          "ci_high": -1.352733175019077,
          "ci_low": -2.995404180366738,
          "coef": -2.1740686776929077,
          "p": 3.728118372407259e-7,
          "se": 0.41717801834785523,
          "t": -5.211369204693105,
          "term": "interaction"
        },
        {
          "ci_high": 1.3489592590195358,
          "ci_low": 0.293920233449696,
          "coef": 0.8214397462346159,
          "p": 0.0023914060858454622,
          "se": 0.26794110843499375,
          "t": 3.065747361547206,
          "term": "treated"
        },
        {
          "ci_high": 0.694998864056584,
          "ci_low": -0.4938017536913518,
          "coef": 0.10059855518261604,
          "p": 0.7392379172187712,
          "se": 0.30191163313181324,
          "t": 0.333205296328198,
          "term": "post"
        },
        {
          "ci_high": -1.108785352233355,
          "ci_low": -2.8672441260078227,
          "coef": -1.988014739120589,
          "p": 0.000012481984273276874,
          "se": 0.44658385288438934,
          "t": -4.451604611945613,
          "term": "bope"
        }
      ]
    }

    Hashes SHA-256 e metadados de reprodução

    Digests dos dados

    ref_panel 08a9abb05cbf0e6ac722e2f9623361707b093718d3946fa0affd09deadbafbfb

    sha256 do resultado

    abf49ef4a208541d74cf7eb415efd19b0ee55f310e217852d971d838b83b4507

    Resultado mostrado ao modelo
28. 27Turno do modelo
    call_bb79b943referenceFim: 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 = -2,174. Reference path transcribed from the item's own estimation script.",
        "caveats": [],
        "figure_refs": [
          "call_526ed370:coef.interaction"
        ]
      }

    Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
29. 28Guarda
    resposta aceita

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    -2,174

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    1 nova(s) tentativa(s) restante(s)
30. 29Resposta

    Estimativa (infer.ols): coef.interaction = -2,174. Reference path transcribed from the item's own estimation script.

    Figuras citadas: call_526ed370:coef.interaction · Parada: answered

    Figura da resposta: call_526ed370:coef.interaction = -2,17407-2.1740686776929077 (se 0,4171780.41717801834785523)
31. 30Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
32. 31Pontuação
    #1figura da resposta próximapassouobrigatória-2.174068678 in [-2.991737594, -1.356399762]?
33. 32Pontuação
    #2figura da resposta próximapassouregistrada-2.174068678 vs -2.1740686776929175
34. 33Pontuação
    #3figura da resposta próximapassouregistrada0.4171780183 vs 0.41717801834785306

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