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Tarefa qrdata_socsci.370

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): rd (regression discontinuity)

Pergunta feita ao analista

How much is the effect of alcohol consumption on death of all causes at 21 years? Please only consider data from people that are no older than 22 years and no younger than 20 years. Please answer with the magnitude of change in the number of deaths and round to the nearest hundredth.

Resposta

Estimativa (infer.rdd): effect = 0,09979. Caminho de referência inferido à mão.

Valores esperados e de onde vêm

qrdata.gold.370

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 370 (0-based), field 'answer'
Trecho
item 370 answer 0.10
Valor
0,10.1
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)
Desvio conhecido
não conferido
The question asks the change in the number of deaths; the local linear jump of `all` at 21 on ages 20-22 is 9.75 deaths per 100,000, while the gold 0.10 equals the jump in log(all) (0.0998). Found by an independent statsmodels fit before any MAGI tool ran.
Endereço
https://raw.githubusercontent.com/xxxiaol/QRData/de450af45ff7101b328bb064c6b475f73414a7ed/benchmark/QRData.json
Licença
CC BY-NC 4.0
  • #1 figura da resposta próxima: [0.097,0.10300000000000001] · interval · [0.097, 0.10300000000000001] ()

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
    0.1 in (0.097, 0.103)?
    obrigatóriapassou[0.097,0.10300000000000001]0,10.1interval · [0.097, 0.10300000000000001] ()qrdata.gold.370call_3cbe569a

    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.rddChamada do agente: infer.rdd (call_3cbe569a)igual
    Argumentos da referência e do agente
    ArgumentoReferênciaAgente
    bandwidth1111igual
    cluster——não fixado pela referência
    cutoff21212121igual
    dataset"drinking_log""drinking_log"igual
    kernel"uniform""uniform"igual
    level—0,950.95não fixado pela referência
    polynomial1111igual
    running"agecell""agecell"igual
    subset——não fixado pela referência
    y"log_all""log_all"igual

    Pendências do caminho de referência: needs_spec:outcome_scale

    Trajetória

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

    1. 0Início

      How much is the effect of alcohol consumption on death of all causes at 21 years? Please only consider data from people that are no older than 22 years and no younger than 20 years. Please answer with the magnitude of change in the number of deaths and round to the nearest hundredth.

      drinking (agent)

      To estimate the impacts of alcohol on death, we could use the fact that legal drinking age imposes a discontinuity on nature. In the US, those just under 21 years don’t drink (or drink much less) while those just older than 21 do drink. The csv file drinking.csv contains mortality data aggregated by age. Each row is the average age of a group of people and the average mortality by all causes (all), by moving vehicle accident (mva) and by suicide (suicide).

      file 3c3a97cca6fdd96f6856f7856b5f8bc786a31d1a9be78c9f610ef3070783e970 · frame —
      drinking_log (reference)
      file 3c3a97cca6fdd96f6856f7856b5f8bc786a31d1a9be78c9f610ef3070783e970 · frame —
      1. derive {"args":{"column":"all"},"name":"log_all","op":"log"}
    2. 1Transformação
      drinking_log · derive {"args":{"column":"all"},"name":"log_all","op":"log"}
      50 → 50 linhas
      Frame antes → depois: d60f201e1a38 → 4b7a4f0300d4
    3. 2Turno do modelo
      call_efaeb706referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache
      Texto visível

      Sharp RD at age 21 on ages 20 to 22 (a uniform kernel of bandwidth 1, both ends inclusive), local linear on each side, on log mortality: the gold is the jump in log(all), not in all.

      Chamadas pedidas
      • infer.rdd
        {
          "bandwidth": 1,
          "cutoff": 21,
          "dataset": "drinking_log",
          "kernel": "uniform",
          "polynomial": 1,
          "running": "agecell",
          "y": "log_all"
        }
    4. 3Ferramenta
      infer.rddv0.1.0call_3cbe569adecidido pelo modeloreexecutável

      Motivo: Sharp RD at age 21 on ages 20 to 22 (a uniform kernel of bandwidth 1, both ends inclusive), local linear on each side, on log mortality: the gold is the jump in log(all), not in all.

      Argumentos
      {
        "bandwidth": 1,
        "cluster": null,
        "cutoff": 21,
        "dataset": "drinking_log",
        "kernel": "uniform",
        "level": 0.95,
        "polynomial": 1,
        "running": "agecell",
        "subset": null,
        "y": "log_all"
      }
      Figuras call_3cbe569a
      ChaveValorValor brutoErro-padrãoIC
      effect
      Jump in log_all at the cutoff of agecell
      0,0997942 0.099794201007424660,01909670.0190966775064627360,0623654 – 0,137223
      Diagnósticos
      • bandwidth = 11
      • bandwidth_method = "user"
      • n_left = 1212
      • n_right = 1212
      • kernel = "uniform"
      • polynomial = 11
      • bandwidth_sensitive = false
      • n_obs = 4848
      • se_type = "HC1"
      Tabelas (1)
      {
        "sensitivity": [
          {
            "bandwidth": 0.5,
            "ci_high": 0.1412768175076522,
            "ci_low": 0.03850556813918095,
            "estimate": 0.08989119282341657,
            "multiplier": 0.5,
            "n_left": 6,
            "n_right": 6,
            "se": 0.026217637206376685
          },
          {
            "bandwidth": 1,
            "ci_high": 0.13722300114446778,
            "ci_low": 0.06236540087038153,
            "estimate": 0.09979420100742466,
            "multiplier": 1,
            "n_left": 12,
            "n_right": 12,
            "se": 0.019096677506462736
          },
          {
            "bandwidth": 2,
            "ci_high": 0.10377936468166427,
            "ci_low": 0.05307581553107505,
            "estimate": 0.07842759010636965,
            "multiplier": 2,
            "n_left": 24,
            "n_right": 24,
            "se": 0.01293481654523561
          }
        ]
      }
      • 2 rows were dropped because one of 'log_all', 'agecell' was missing
      Digests dos dados
      drinking_log 4b7a4f0300d4de0e81e79acff3018fe712690c9427eaadc45ae7245194f8f2d3
      sha256 do resultado
      5338fb344e6700a817569fcc0f7bcca254d5129bb6c0b271bb1d3c037ea3d260
    5. 4Turno do modelo
      call_3467a1e6referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache
      Chamadas pedidas
      • submit_answer (meta-ferramenta)
        {
          "answer": "Estimativa (infer.rdd): effect = 0,09979. Caminho de referência inferido à mão.",
          "caveats": [],
          "figure_refs": [
            "call_3cbe569a:effect"
          ]
        }
    6. 5Guarda
      resposta aceita
      Números encontrados
      0,09979
      Sem lastro
      —
      Não citados
      —
      1 nova(s) tentativa(s) restante(s)
    7. 6Resposta

      Estimativa (infer.rdd): effect = 0,09979. Caminho de referência inferido à mão.

      Figuras citadas: call_3cbe569a:effect · Parada: answered
      Figura da resposta: call_3cbe569a:effect = 0,09979420.09979420100742466 (se 0,01909670.019096677506462736)
    8. 7Pontuação
      #0resposta com lastropassouobrigatóriaevery number backed
    9. 8Pontuação
      #1figura da resposta próximapassouobrigatória0.1 in (0.097, 0.103)?

    Épocas × verificações

    Resultado de cada verificação em cada época pontuada
    #01
    Época 1passoupassou

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