Tarefa causcibench_real.q116

Veredito

Pergunta feita ao analista

How much of global total economic damages due to extreme weather is attributable to climate change? (in thousands)

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
   newman_global_costs— The study examines the global economic costs of extreme weather events attributable to climate change using Event Attribution methodology. It analyzes 185 extreme weather events from 2000-2019 across multiple countries, combining data from climate attribution studies with socio-economic impact data from the Emergency Management Database (EM-DAT). The methodology uses climate models to compare observed extreme weather events with counterfactual scenarios without anthropogenic greenhouse gas emissions. This generates Fraction of Attributable Risk (FAR) estimates quantifying what portion of each event's risk is attributable to human-induced climate change. These FAR values are then applied to observed mortality and economic damages to calculate climate change-attributable costs globally. Variable description: - Event Identifier: Identifies the specific extreme weather event - Year: Year of event - Country/region: Country of event - Continent: Continent where event happened - Disaster Type: One of Flood, Heatwave, Wildfire, Drought, Storm, Cold wave - FAR Best Estimate: FAR value = 1 - p_1/p_0, where p_1 is the probability of event in the real world, and p_0 in counterfactual world without climate change. - Total Deaths: Total number of deaths caused by event - Total Affected - Total Damage (US$ Thousands): Amount of damage caused in thousands of US dollars per event - Insured Loss (US$ Thousands): Amount of economic damage insured - Uninsured damages: Difference of previous two - Total Damage (US$ Millions): Amount of damage caused in millions of US dollars per event - Insured Loss (US$ Millions): Amount of economic damage insured (in millions)
2. 2Chamada que gerou a resposta finalinfer.ols(Regressão linear por mínimos quadrados ordinários (MQO))citada na respostacoincide com a referência

   Ver chamada call_a24ccc8d nos detalhes

   causcibench_metadata_rule/1: ols

   Por que este é o caminho de referência: causcibench_metadata_rule/1: ols
   Argumentos principais

   dataset:

   "newman_global_costs"

   intercept:

   true

   level:

   0,950.95

   se:

   "HC1"

   x:

   ["FAR Best Estimate"]

   y:

   "Total Damage (US$ Thousands)"

   Resultado calculado

   • coef.FAR Best Estimate = 2.485.720 2485720.7916303845
     Coefficient on FAR Best Estimate in the regression of Total Damage (US$ Thousands)
   Diagnósticos: n_obs = 116116 (observations used) · k = 22 (parameters estimated, fixed-effect dummies included) · r2 = 0,007724940.007724937614798977 · df_resid = 114114 · se_type = "HC1" (heteroskedasticity-robust standard errors (HC1))
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: 2485720
   Figuras citadas: call_a24ccc8d:coef.FAR Best Estimate
   • ✓
     #0resposta com lastro

     every number in the answer was stated by a tool call
   • ✗
     #1figura da resposta próxima

     CauSciBench effect accuracy: |answer figure − gold| ≤ 5% of |gold|

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 much of global total economic damages due to extreme weather is attributable to climate change? (in thousands)

   newman_global_costs (agent)

   The study examines the global economic costs of extreme weather events attributable to climate change using Event Attribution methodology. It analyzes 185 extreme weather events from 2000-2019 across multiple countries, combining data from climate attribution studies with socio-economic impact data from the Emergency Management Database (EM-DAT). The methodology uses climate models to compare observed extreme weather events with counterfactual scenarios without anthropogenic greenhouse gas emissions. This generates Fraction of Attributable Risk (FAR) estimates quantifying what portion of each event's risk is attributable to human-induced climate change. These FAR values are then applied to observed mortality and economic damages to calculate climate change-attributable costs globally. Variable description: - Event Identifier: Identifies the specific extreme weather event - Year: Year of event - Country/region: Country of event - Continent: Continent where event happened - Disaster Type: One of Flood, Heatwave, Wildfire, Drought, Storm, Cold wave - FAR Best Estimate: FAR value = 1 - p_1/p_0, where p_1 is the probability of event in the real world, and p_0 in counterfactual world without climate change. - Total Deaths: Total number of deaths caused by event - Total Affected - Total Damage (US$ Thousands): Amount of damage caused in thousands of US dollars per event - Insured Loss (US$ Thousands): Amount of economic damage insured - Uninsured damages: Difference of previous two - Total Damage (US$ Millions): Amount of damage caused in millions of US dollars per event - Insured Loss (US$ Millions): Amount of economic damage insured (in millions)

   file b5566c38a3ac6ecba6cc014aea2af8aaa376b6fed658c546a704080cb4a8240d · frame —
2. 1Turno do modelo
   call_df3e237dreferenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Texto visível

   causcibench_metadata_rule/1: ols

   Chamadas pedidas

   • infer.ols

     {
       "controls": [],
       "dataset": "newman_global_costs",
       "x": [
         "FAR Best Estimate"
       ],
       "y": "Total Damage (US$ Thousands)"
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
3. 2Ferramenta
   infer.ols— Regressão linear por mínimos quadrados ordinários (MQO)v0.1.0call_a24ccc8ddecidido pelo modeloreexecutável

   Motivo: causcibench_metadata_rule/1: ols

   Argumentos

   {
     "cluster": null,
     "controls": [],
     "dataset": "newman_global_costs",
     "fe": [],
     "intercept": true,
     "level": 0.95,
     "se": "HC1",
     "subset": null,
     "weights": null,
     "x": [
       "FAR Best Estimate"
     ],
     "y": "Total Damage (US$ Thousands)"
   }

   Figuras call_a24ccc8d

   Chave	Valor	Valor bruto	Erro-padrão	IC
   coef.FAR Best Estimate Coefficient on FAR Best Estimate in the regression of Total Damage (US$ Thousands)	2.485.720	2485720.7916303845	2.238.3702238368.5876999022	-1.948.470 – 6.919.910

   Diagnósticos

   • n_obs = 116116 (observations used)
   • k = 22 (parameters estimated, fixed-effect dummies included)
   • r2 = 0,007724940.007724937614798977
   • df_resid = 114114
   • se_type = "HC1" (heteroskedasticity-robust standard errors (HC1))
   • n_clusters = — (errors not clustered)

   Derivação matemática e pressupostos

   coef.FAR Best Estimate

   1. Coeficiente de FAR Best Estimate na regressão por mínimos quadrados ordinários de Total Damage (US$ Thousands).→ 2.485.720 (2485720.7916303845)
   2. Erro-padrão robusto à heterocedasticidade (HC1): a raiz da variância estimada do coeficiente.ep = √V_jj→ 2.238.370 (2238368.5876999022)
   3. Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 114 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 observations; their variance may differ

   Tabelas (1)

   {
     "coefficients": [
       {
         "ci_high": 5116862.193457595,
         "ci_low": 1376350.1531758988,
         "coef": 3246606.173316747,
         "p": 0.0008167544203470741,
         "se": 944100.601527751,
         "t": 3.438834980152606,
         "term": "Intercept"
       },
       {
         "ci_high": 6919911.723894987,
         "ci_low": -1948470.1406342182,
         "coef": 2485720.7916303845,
         "p": 0.2691180557229439,
         "se": 2238368.5876999022,
         "t": 1.110505573250854,
         "term": "FAR Best Estimate"
       }
     ]
   }

   • 75 rows were dropped because one of 'Total Damage (US$ Thousands)', 'FAR Best Estimate' was missing

   Hashes SHA-256 e metadados de reprodução

   Digests dos dados

   newman_global_costs 6d5cad51d45205e9a2becb995681807ee23c75f78a49e411ace6a31ff6cc0d24

   sha256 do resultado

   727f6b5d6a01f795d493816306055c6b082a636fbbc3da35929d816610b65cc2

   Resultado mostrado ao modelo
4. 3Turno do modelo
   call_5dcbdba4referenceFim: 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.FAR Best Estimate = 2485720.",
       "caveats": [],
       "figure_refs": [
         "call_a24ccc8d:coef.FAR Best Estimate"
       ]
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
5. 4Guarda
   resposta aceita

   Números encontrados

   2485720

   Sem lastro

   —

   Não citados

   —

   1 nova(s) tentativa(s) restante(s)
6. 5Resposta

   Estimativa (infer.ols): coef.FAR Best Estimate = 2485720.

   Figuras citadas: call_a24ccc8d:coef.FAR Best Estimate · Parada: answered

   Figura da resposta: call_a24ccc8d:coef.FAR Best Estimate = 2.485.7202485720.7916303845 (se 2.238.3702238368.5876999022)
7. 6Pontuação
   #0resposta com lastropassouobrigatóriaevery number backed
8. 7Pontuação
   #1figura da resposta próximafalhouobrigatória2485720.792 vs -1413836.076

Épocas × verificações

Tentativas

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

• época 1, tentativa 1falhoupontuada(exibindo)

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