Pular para o conteúdo

Tarefa crb_papers.q001

crb_papersagentT2publicépoca 1, tentativa 1Execução: 20261015T171027Z-suite-crb_papers-naive-54c3f2

Veredito

falhouDecidida pela verificação 1: figura da resposta próxima, fora da tolerância.
2.661306316 in [-0.9011559908, 0.7341933239]?

Família do método (das chamadas de ferramentas): ols (regression)

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?

Resposta

Estimativa ingênua (infer.ols): coef.ucdp_lead = 2,661.

Valores esperados e de onde vêm

crb_papers.q001.gold

Fonte
CausalReasoningBenchmark, causal_queries.json, index 1 (revision edd0fe1c1d1c)
Tipo
benchmark_gold
Localização
causal_queries.json['1'].effect and .standard_error
Trecho
index 1: effect -0.0834813, standard_error 0.417181
Valor como publicado
-0.08348133345223285
Erro-padrão como publicado
0.4171809476066692
Valor
-0,0834813-0.08348133345223285 (se 0,4171810.4171809476066692)
Conferido por
arquivo do benchmark · Read from the catalog at the pinned revision by scripts/freeze_crb_papers.py; the catalog states the value its estimation script prints.
Endereço
https://huggingface.co/datasets/syrgkanislab/CausalReasoningBenchmark/resolve/edd0fe1c1d1c5c0a4f0e3e67a50481ac679fb888/causal_queries.json
Licença
MIT
  • #2 figura da resposta próxima: -0,0834813-0.08348133345223285 · relativa 0.000001 · absoluta 4.1718094760666917e-7
  • #3 figura da resposta próxima: 0,4171810.4171809476066692 · relativa 0.000001 · absoluta 4.1718094760666917e-7

crb_papers.q001.script

Fonte
CausalReasoningBenchmark reference estimation script for index 1
Tipo
software
Localização
solutions/research_papers/DiD/Blair_etal_2022/question_1/estimation.py
Valor
—
Conferido por
arquivo do benchmark · sha256 of the script's 1899 bytes at the pinned revision; the reference path of the item is a transcription of this script into typed transforms and tool calls (never executed here).
Implementação
independente da ferramenta
sha256 da cópia lida
dd3e081e9cc3fa7325e2b2c30704a80eecae8d7bf024a367ae3adb399f80f0a3 (identifica a cópia lida, não um arquivo canônico)
Endereço
https://huggingface.co/datasets/syrgkanislab/CausalReasoningBenchmark/resolve/edd0fe1c1d1c5c0a4f0e3e67a50481ac679fb888/solutions/research_papers/DiD/Blair_etal_2022/question_1/estimation.py
Licença
MIT

    Verificações

    Cada verificação, o que esperava, o que observou e a chamada que leu
    #VerificaçãoObrigatóriaResultadoEsperadoObservadoTolerânciaFonteChamada
    0resposta com lastro
    every number backed
    obrigatóriapassou—[]———
    1figura da resposta próxima
    2.661306316 in [-0.9011559908, 0.7341933239]?
    obrigatóriafalhou[-0.9011559907613045,0.7341933238568388]2,661312.661306316484741interval · [-0.9011559907613045, 0.7341933238568388]—call_439cef0f
    2figura da resposta próxima
    2.661306316 vs -0.08348133345223285
    registradafalhou-0,0834813-0.083481333452232852,661312.661306316484741relativa 0.000001 · absoluta 4.1718094760666917e-7crb_papers.q001.goldcall_439cef0f
    3figura da resposta próxima
    0.2835885146 vs 0.4171809476066692
    registradafalhou0,4171810.41718094760666920,2835890.28358851463203155relativa 0.000001 · absoluta 4.1718094760666917e-7crb_papers.q001.goldcall_439cef0f

    Caminho de referência e caminho do agente

    A chamada de referência ao lado da chamada que deu a resposta, argumento por argumento.

    caminho de referência do item · benchmark_script

    Referência: infer.olsChamada do agente: infer.ols (call_439cef0f)igual
    Argumentos da referência e do agente
    ArgumentoReferênciaAgente
    cluster"country"—diferente
    controls["treated","post"][]diferente
    dataset"ref_panel""ds_0001"diferente
    fe—[]não fixado pela referência
    intercept—truenão fixado pela referência
    level—0,950.95não fixado pela referência
    se—"HC1"não fixado pela referência
    subset——não fixado pela referência
    weights——não fixado pela referência
    x["interaction"]["ucdp_lead"]diferente
    y"y""dv"diferente

    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. 1Turno do modelo
      call_b1691198naive-olsFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache
      Texto visível

      Linha de base ingênua: regressão do resultado no tratamento, sem controles.

      Chamadas pedidas
      • infer.ols
        {
          "dataset": "ds_0001",
          "x": [
            "ucdp_lead"
          ],
          "y": "dv"
        }
    3. 2Ferramenta
      infer.olsv0.1.0call_439cef0fdecidido pelo modeloreexecutável

      Motivo: Linha de base ingênua: regressão do resultado no tratamento, sem controles.

      Argumentos
      {
        "cluster": null,
        "controls": [],
        "dataset": "ds_0001",
        "fe": [],
        "intercept": true,
        "level": 0.95,
        "se": "HC1",
        "subset": null,
        "weights": null,
        "x": [
          "ucdp_lead"
        ],
        "y": "dv"
      }
      Figuras call_439cef0f
      ChaveValorValor brutoErro-padrãoIC
      coef.ucdp_lead
      Coefficient on ucdp_lead in the regression of dv
      2,66131 2.6613063164847410,2835890.283588514632031552,10527 – 3,21734
      Diagnósticos
      • n_obs = 3.1863186
      • k = 22
      • r2 = 0,02534050.02534047668387529
      • df_resid = 3.1843184
      • se_type = "HC1"
      • n_clusters = —
      Tabelas (1)
      {
        "coefficients": [
          {
            "ci_high": 9.470788605928105,
            "ci_low": 8.807655767880265,
            "coef": 9.139222186904185,
            "p": 0,
            "se": 0.16910534117379913,
            "t": 54.04455071298599,
            "term": "Intercept"
          },
          {
            "ci_high": 3.217340961045238,
            "ci_low": 2.1052716719242435,
            "coef": 2.661306316484741,
            "p": 1.1663999211684164e-20,
            "se": 0.28358851463203155,
            "t": 9.38439386354522,
            "term": "ucdp_lead"
          }
        ]
      }
      Digests dos dados
      ds_0001 6fc175d00f591712a12119577295b02c2ba59fe5887bf75a4f7ace2838ffc2e2
      sha256 do resultado
      237aded3a7ba495cf5531a51fabbb36f2924190a9833b3d348c4515e8f7ce35d
    4. 3Turno do modelo
      call_89facfbcnaive-olsFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache
      Chamadas pedidas
      • submit_answer (meta-ferramenta)
        {
          "answer": "Estimativa ingênua (infer.ols): coef.ucdp_lead = 2,661.",
          "caveats": [],
          "figure_refs": [
            "call_439cef0f:coef.ucdp_lead"
          ]
        }
    5. 4Guarda
      resposta aceita
      Números encontrados
      2,661
      Sem lastro
      —
      Não citados
      —
      1 nova(s) tentativa(s) restante(s)
    6. 5Resposta

      Estimativa ingênua (infer.ols): coef.ucdp_lead = 2,661.

      Figuras citadas: call_439cef0f:coef.ucdp_lead · Parada: answered
      Figura da resposta: call_439cef0f:coef.ucdp_lead = 2,661312.661306316484741 (se 0,2835890.28358851463203155)
    7. 6Pontuação
      #0resposta com lastropassouobrigatóriaevery number backed
    8. 7Pontuação
      #1figura da resposta próximafalhouobrigatória2.661306316 in [-0.9011559908, 0.7341933239]?
    9. 8Pontuação
      #2figura da resposta próximafalhouregistrada2.661306316 vs -0.08348133345223285
    10. 9Pontuação
      #3figura da resposta próximafalhouregistrada0.2835885146 vs 0.4171809476066692

    Épocas × verificações

    Resultado de cada verificação em cada época pontuada
    #0123
    Época 1passoufalhoufalhoufalhou

    Tentativas

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

    Downloads desta tarefa

    Pacote da tarefa (.zip)
    104,9 KB · sha256 b84241dde4d37504988dbe25e2c68d5a76aba7ec779b5663a0659208c025f7bb

    O hash de um zip é só de transporte: o que ancora a integridade é o RUN_SHA256SUMS que vai dentro dele, cópia exata do SHA256SUMS da execução.

    Downloads desta tarefa
    ArquivoTamanhosha256
    attachments/08248f5e93af52048a7e72fd68ea872012275580941d216c7d23111707545d4e.json265 B08248f5e93af52048a7e72fd68ea872012275580941d216c7d23111707545d4e
    attachments/14ce74e068a942e120580bcbc20a1e5f2ecf79272d98a29647e67c36c9495870.json3,6 KB14ce74e068a942e120580bcbc20a1e5f2ecf79272d98a29647e67c36c9495870
    attachments/277b7d59487652bc54f2c22c8206d8cfef2c561a831e8159a310280447bc5487.json963 B277b7d59487652bc54f2c22c8206d8cfef2c561a831e8159a310280447bc5487
    attachments/433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420.json2,4 KB433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420
    attachments/52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b.json64,0 KB52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b
    attachments/8386c5e6d199df9608f3035c960cba926d79577df51cb9e8209d40f734077e7a.json857 B8386c5e6d199df9608f3035c960cba926d79577df51cb9e8209d40f734077e7a
    attachments/99798e9a7477604c7d7d38b6c71658b877f158b3c049f9a01de2e956ef4ea5ab.json224 B99798e9a7477604c7d7d38b6c71658b877f158b3c049f9a01de2e956ef4ea5ab
    native/crb_papers/crb_papers.q001.e1.a1.json4,1 KB90672fd652d16e7a4b1e94fb19ff95efb46fd7711930ca38f3505decdc79ce61
    sources/crb_papers.q001.gold.json1,1 KBf94b1cb4c5213c4c094560cf14adf1a1f7861fc70d814c2762854bcb3548bb1a
    sources/crb_papers.q001.script.json1,1 KB6fc299a4e335e96d097faef7c25caf91f92d74495f0576a47e22a235e19f4483
    specs/crb_papers.q001.json12,0 KB26eb67d70df3201d304c5f5dfc9829820965cf98d602f1c059bf84f0950bedc2
    traces/crb_papers.q001.e1.a1.jsonl14,3 KBe59962e3f18391a4b2a2801df20374dd15476b36db713b5ce007f7b582fa39c4
    trajectories/crb_papers.q001.e1.a1.jsonl15,4 KB8cb7a3c094a820e60c67b6db9afbefd04ae4a9b0b92b7c4448af3010df2ffb9f