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Tarefa qrdata_socsci.380

qrdata_socsciagentT2publicépoca 1, tentativa 1Execução: 20261015T170010Z-suite-qrdata_socsci-scripted-7912b3

Veredito

passouPassou em todas as verificações obrigatórias.

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

Pergunta feita ao analista

What is the difference in the proportion of full-time employees in NJ and PA? Please round to the nearest thousandth.

Resposta

Estimativa (infer.ols): coef.nj = 0,04812. Caminho de referência inferido à mão.

Valores esperados e de onde vêm

qrdata.gold.380

Fonte
QRData benchmark/QRData.json at de450af45ff7101b328bb064c6b475f73414a7ed (Xiao Liu, Zirui Wu, Xueqing Wu, Pan Lu, Kai-Wei Chang, Yansong Feng. Are LLMs Capable of Data-based Statistical and Causal Reasoning? Benchmarking Advanced Quantitative Reasoning with Data. Findings of the Association for Computational Linguistics: ACL 2024, pages 9215-9235.)
Tipo
benchmark_gold
Localização
QRData.json item 380 (0-based), field 'answer'
Trecho
item 380 answer 0.048
Valor
0,0480.048
Conferido por
arquivo do benchmark · The printed gold is in the excerpt and index.json. It has fewer than 3 significant digits, so it is not set as value_as_published: the isolation gate searches that field verbatim, and a string this short can occur anywhere in a prompt (the gate does not search such numbers either).
sha256 da cópia lida
e076d960f19434adf728aab70480ce34808c06066fcea48eaa38b145d1a111a1 (identifica a cópia lida, não um arquivo canônico)
Endereço
https://raw.githubusercontent.com/xxxiaol/QRData/de450af45ff7101b328bb064c6b475f73414a7ed/benchmark/QRData.json
Licença
CC BY-NC 4.0
  • #1 figura da resposta próxima: [0.04656,0.049440000000000005] · interval · [0.04656, 0.049440000000000005] ()

qrdata.eval_py

Fonte
QRData benchmark/eval.py at de450af45ff7101b328bb064c6b475f73414a7ed (Xiao Liu, Zirui Wu, Xueqing Wu, Pan Lu, Kai-Wei Chang, Yansong Feng. Are LLMs Capable of Data-based Statistical and Causal Reasoning? Benchmarking Advanced Quantitative Reasoning with Data. Findings of the Association for Computational Linguistics: ACL 2024, pages 9215-9235.)
Tipo
software
Localização
benchmark/eval.py: extract_first_number, calc_acc
Valor
—
Conferido por
arquivo do benchmark · Vendored byte for byte as suites/data/qrdata_socsci/vendor/benchmark_eval.py.txt; a parity test runs its functions against MAGI's port and the answer_figure_close check.
Implementação
independente da ferramenta
sha256 da cópia lida
d6e5c1f7535ac9413951996c15544beb6f184244acb2da56e1fcd0942837e33d (identifica a cópia lida, não um arquivo canônico)
Endereço
https://raw.githubusercontent.com/xxxiaol/QRData/de450af45ff7101b328bb064c6b475f73414a7ed/benchmark/eval.py
Licença
CC BY-NC 4.0

    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
    0.048 in (0.04656, 0.04944)?
    obrigatóriapassou[0.04656,0.049440000000000005]0,0480.048interval · [0.04656, 0.049440000000000005] ()qrdata.gold.380call_0fcf28c0

    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 · hand_inferred

    Referência: infer.olsChamada do agente: infer.ols (call_0fcf28c0)igual
    Argumentos da referência e do agente
    ArgumentoReferênciaAgente
    cluster——não fixado pela referência
    controls—[]não fixado pela referência
    dataset"minwage_props""minwage_props"igual
    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["nj"]["nj"]igual
    y"fullPropAfter""fullPropAfter"igual

    Trajetória

    Cada evento na ordem em que aconteceu: turnos do modelo, chamadas de ferramentas, guardas, transformações e pontuação.

    1. 0Início

      What is the difference in the proportion of full-time employees in NJ and PA? Please round to the nearest thousandth.

      minwage (agent)

      Two social science researchers examined the impact of raising the minimum wage on employment in the fast-food industry. In 1992, the state of New Jersey (NJ) in the United States raised the minimum wage from $4.25 to $5.05 per hour. Did such an increase in the minimum wage reduce employment as economic theory predicts? As discussed above, answering this question requires inference about the NJ employment rate in the absence of such a raise in the minimum wage. Since this counterfactual outcome is not observable, we must somehow estimate it using observed data. One possible strategy is to look at another state in which the minimum wage did not increase. For example, the researchers of this study chose the neighboring state, Pennsylvania (PA), on the grounds that NJ’s economy resembles that of Pennsylvania, and hence the fast-food restaurants in the two states are comparable. Under this cross-section comparison design, therefore, the fast-food restaurants in NJ serve as the treatment group receiving the treatment (i.e., the increase in the minimum wage), whereas those in PA represent the control group, which did not receive such a treatment. To collect pretreatment and outcome measures, the researchers surveyed the fast-food restaurants before and after the minimum wage increase. Specifically, they gathered information about the number of full-time employees, the number of part-time employees, and their hourly wages, for each restaurant. The CSV file minwage.csv contains this data set. Variable Description chain: name of the fast-food restaurant chain location: location of the restaurants (centralNJ, northNJ, PA, shoreNJ, southNJ) wageBefore: wage before the minimum-wage increase wageAfter: wage after the minimum-wage increase fullBefore: number of full-time employees before the minimum-wage increase fullAfter: number of full-time employees after the minimum-wage increase partBefore: number of part-time employees before the minimum-wage increase partAfter: number of part-time employees after the minimum-wage increase

      file 3c3a97cca6fdd96f6856f7856b5f8bc786a31d1a9be78c9f610ef3070783e970 · frame —
      minwage_props (reference)
      file 3c3a97cca6fdd96f6856f7856b5f8bc786a31d1a9be78c9f610ef3070783e970 · frame —
      1. derive {"args":{"columns":["fullAfter","partAfter"]},"name":"totalAfter","op":"sum"}
      2. derive {"args":{"left":"fullAfter","right":"totalAfter"},"name":"fullPropAfter","op":"ratio"}
      3. derive {"args":{"columns":["fullBefore","partBefore"]},"name":"totalBefore","op":"sum"}
      4. derive {"args":{"left":"fullBefore","right":"totalBefore"},"name":"fullPropBefore","op":"ratio"}
      5. derive {"args":{"left":"fullPropAfter","right":"fullPropBefore"},"name":"fullPropChange","op":"diff"}
      6. derive {"args":{"cmp":"ne","column":"location","value":"PA"},"name":"nj","op":"indicator"}
    2. 1Transformação
      minwage_props · derive {"args":{"columns":["fullAfter","partAfter"]},"name":"totalAfter","op":"sum"}
      358 → 358 linhas
      Frame antes → depois: 55dec890a044 → 808a12f40fca
    3. 2Transformação
      minwage_props · derive {"args":{"left":"fullAfter","right":"totalAfter"},"name":"fullPropAfter","op":"ratio"}
      358 → 358 linhas
      Frame antes → depois: 808a12f40fca → 891fe31e6dd3
    4. 3Transformação
      minwage_props · derive {"args":{"columns":["fullBefore","partBefore"]},"name":"totalBefore","op":"sum"}
      358 → 358 linhas
      Frame antes → depois: 891fe31e6dd3 → fc02f54744c1
    5. 4Transformação
      minwage_props · derive {"args":{"left":"fullBefore","right":"totalBefore"},"name":"fullPropBefore","op":"ratio"}
      358 → 358 linhas
      Frame antes → depois: fc02f54744c1 → 590bda545f53
    6. 5Transformação
      minwage_props · derive {"args":{"left":"fullPropAfter","right":"fullPropBefore"},"name":"fullPropChange","op":"diff"}
      358 → 358 linhas
      Frame antes → depois: 590bda545f53 → 3ab27676376a
    7. 6Transformação
      minwage_props · derive {"args":{"cmp":"ne","column":"location","value":"PA"},"name":"nj","op":"indicator"}
      358 → 358 linhas
      Frame antes → depois: 3ab27676376a → 67fcfadd313b
    8. 7Turno do modelo
      call_f8eb79afreferenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache
      Texto visível

      OLS of the after-increase proportion of full-time employees on the NJ indicator: NJ mean minus PA mean (QSS 2.5.1).

      Chamadas pedidas
      • infer.ols
        {
          "dataset": "minwage_props",
          "x": [
            "nj"
          ],
          "y": "fullPropAfter"
        }
    9. 8Ferramenta
      infer.olsv0.1.0call_0fcf28c0decidido pelo modeloreexecutável

      Motivo: OLS of the after-increase proportion of full-time employees on the NJ indicator: NJ mean minus PA mean (QSS 2.5.1).

      Argumentos
      {
        "cluster": null,
        "controls": [],
        "dataset": "minwage_props",
        "fe": [],
        "intercept": true,
        "level": 0.95,
        "se": "HC1",
        "subset": null,
        "weights": null,
        "x": [
          "nj"
        ],
        "y": "fullPropAfter"
      }
      Figuras call_0fcf28c0
      ChaveValorValor brutoErro-padrãoIC
      coef.nj
      Coefficient on nj in the regression of fullPropAfter
      0,0481189 0.048118861422914130,03347790.03347789248583884-0,0177204 – 0,113958
      Diagnósticos
      • n_obs = 358358
      • k = 22
      • r2 = 0,005621660.005621660177508314
      • df_resid = 356356
      • se_type = "HC1"
      • n_clusters = —
      Tabelas (1)
      {
        "coefficients": [
          {
            "ci_high": 0.33140603923428874,
            "ci_low": 0.2131581844819965,
            "coef": 0.2722821118581426,
            "p": 9.019998230055255e-18,
            "se": 0.030063268766730506,
            "t": 9.05696961866846,
            "term": "Intercept"
          },
          {
            "ci_high": 0.11395815851989526,
            "ci_low": -0.01772043567406699,
            "coef": 0.04811886142291413,
            "p": 0.1515018869658016,
            "se": 0.03347789248583884,
            "t": 1.437332455836891,
            "term": "nj"
          }
        ]
      }
      Digests dos dados
      minwage_props 67fcfadd313ba1d19f472b3dfda49f69f3c7884474526b8969637c189accc2da
      sha256 do resultado
      c7d67b09c8372523a36282c9b3526d7a9cccff18b9ab5a4ad0159d13618ac47a
    10. 9Turno do modelo
      call_2c7c99e1referenceFim: 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.nj = 0,04812. Caminho de referência inferido à mão.",
          "caveats": [],
          "figure_refs": [
            "call_0fcf28c0:coef.nj"
          ]
        }
    11. 10Guarda
      resposta aceita
      Números encontrados
      0,04812
      Sem lastro
      —
      Não citados
      —
      1 nova(s) tentativa(s) restante(s)
    12. 11Resposta

      Estimativa (infer.ols): coef.nj = 0,04812. Caminho de referência inferido à mão.

      Figuras citadas: call_0fcf28c0:coef.nj · Parada: answered
      Figura da resposta: call_0fcf28c0:coef.nj = 0,04811890.04811886142291413 (se 0,03347790.03347789248583884)
    13. 12Pontuação
      #0resposta com lastropassouobrigatóriaevery number backed
    14. 13Pontuação
      #1figura da resposta próximapassouobrigatória0.048 in (0.04656, 0.04944)?

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    #01
    Época 1passoupassou

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