Tarefa qrdata_socsci.401
qrdata_socsciagentT2publicépoca 1, tentativa 1Execução: 20261015T170025Z-suite-qrdata_socsci-baseline-2a40bfVeredito
no answer figure: the answer cites no effect of an estimator callPergunta feita ao analista
What is the average causal effect of the Neighbors message on whether the voter voted in the 2006 primary election if the voter's age was 65 in 2006? Please estimate a linear regression model with both message and age as predictors, and round the result to the nearest thousandth.
Pelas regras deste roteador não identifiquei um desenho que responda à pergunta; nenhuma estimativa foi feita.
Valores esperados e de onde vêm
qrdata.gold.401
- 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 401 (0-based), field 'answer'
- Trecho
item 401 answer 0.089
- Valor
- 0,089
0.089 - 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 conferidoThe question fixes age in 2006; the gold 0.089 matches age computed as 2008 - yearofbirth (the textbook's convention: 0.0894), while age 65 in 2006 gives 0.0907. Found by an independent statsmodels fit before any MAGI tool ran. At the question's age, 0.0907 rounds to 0.091 and still passes (B-join review).
- Endereço
https://raw.githubusercontent.com/xxxiaol/QRData/de450af45ff7101b328bb064c6b475f73414a7ed/benchmark/QRData.json- Licença
- CC BY-NC 4.0
- #1 figura da resposta próxima:
[0.08632999999999999,0.09167]· interval · [0.08632999999999999, 0.09167] ()
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
| # | Verificação | Obrigatória | Resultado | Esperado | Observado | Tolerância | Fonte | Chamada |
|---|---|---|---|---|---|---|---|---|
| 0 | resposta com lastro every number backed | obrigatória | passou | — | [] | — | — | — |
| 1 | figura da resposta próxima no answer figure: the answer cites no effect of an estimator call | obrigatória | falhou | [0.08632999999999999,0.09167] | — | interval · [0.08632999999999999, 0.09167] () | qrdata.gold.401 | — |
Caminho de referência e caminho do agente
A chamada de referência ao lado da chamada que deu a resposta, argumento por argumento.
Nenhum caminho de referência registrado para esta tarefa.
Trajetória
Cada evento na ordem em que aconteceu: turnos do modelo, chamadas de ferramentas, guardas, transformações e pontuação.
- 0Início
What is the average causal effect of the Neighbors message on whether the voter voted in the 2006 primary election if the voter's age was 65 in 2006? Please estimate a linear regression model with both message and age as predictors, and round the result to the nearest thousandth.
social(agent)Three social scientists conducted an RCT in which they investigated whether social pressure within neighborhoods increases participation. Specifically, during a primary election in the state of Michigan, they randomly assigned registered voters to receive different get-out-the-vote (GOTV) messages and examined whether sending postcards with these messages increased turnout. The researchers exploited the fact that the turnout of individual voters is public information in the United States. The GOTV message of particular interest was designed to induce social pressure by telling voters that after the election their neighbors would be informed about whether they voted in the election or not. The researchers hypothesized that such a namingand-shaming GOTV strategy would increase participation. There are three treatment groups: voters who receive either the social pressure message, the civic duty message, or the Hawthorne effect message. The Hawthorne effect refers to the phenomenon where study subjects behave differently because they know they are being observed by researchers. The experiment also has a control group which consists of those voters receiving no message. The researchers randomly assigned each voter to one of the four groups and examined whether the voter turnout was different across the groups. The data is in the file social.csv. Variable Description hhsize: household size of the voter messages: GOTV messages the voter received (Civic Duty, Control, Neighbors, Hawthorne) sex: sex of the voter (female or male) yearofbirth: year of birth of the voter primary2004: whether the voter voted in the 2004 primary election (1=voted, 0=abstained) primary2006: whether the voter turned out in the 2006 primary election (1=voted, 0=abstained)
file3c3a97cca6fdd96f6856f7856b5f8bc786a31d1a9be78c9f610ef3070783e970· frame — - 1Turno do modelo
call_e0ad0c46rules-1Fim:tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cacheChamadas pedidassubmit_answer(meta-ferramenta){ "answer": "Pelas regras deste roteador não identifiquei um desenho que responda à pergunta; nenhuma estimativa foi feita.", "caveats": [], "figure_refs": [] }
- 2Guardaresposta aceita
- Números encontrados
- —
- Sem lastro
- —
- Não citados
- —
1 nova(s) tentativa(s) restante(s) - 3Resposta
Pelas regras deste roteador não identifiquei um desenho que responda à pergunta; nenhuma estimativa foi feita.
Figuras citadas:—· Parada:answered - 4Pontuação#0resposta com lastropassouobrigatória
every number backed - 5Pontuação#1figura da resposta próximafalhouobrigatória
no answer figure: the answer cites no effect of an estimator call
Épocas × verificações
| # | 0 | 1 |
|---|---|---|
| Época 1 | passou | falhou |
Tentativas
Cada tentativa é seu próprio arquivo e é publicada; só a última de cada época é pontuada.
- época 1, tentativa 1falhoupontuada(exibindo)
Downloads desta tarefa
707e7b3b89a4d371d384fa1fe9f5fff61e325f81c13646b8e68ecf04f3036368O 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.
| Arquivo | Tamanho | sha256 |
|---|---|---|
| attachments/0c10dd5ee1d7e142f49a7a3bc00c421b4b3ae39be3af452d30ed229e95c0374a.json | 2,1 KB | 0c10dd5ee1d7e142f49a7a3bc00c421b4b3ae39be3af452d30ed229e95c0374a |
| attachments/433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420.json | 2,4 KB | 433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420 |
| attachments/52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b.json | 64,0 KB | 52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b |
| attachments/8334c00e056845aca24bacb835d56ec7a6a9dac98cb142bf58d701f948b05913.json | 305 B | 8334c00e056845aca24bacb835d56ec7a6a9dac98cb142bf58d701f948b05913 |
| native/qrdata_socsci/qrdata_socsci.401.e1.a1.json | 311 B | 2db5275d3edde477398a199597f9478e2942e11b6d4e7c0605a9392c2bf07664 |
| sources/qrdata.eval_py.json | 1,3 KB | d610b61f390d29ce767e08ce850eab5cb0dc4d13c1edb62d8cf3b0e49a3a291a |
| sources/qrdata.gold.401.json | 1,7 KB | f1d7f863a87a2cb12604d1a8813b54abf9afaf491584ebc3310d6eb8489b5be4 |
| specs/qrdata_socsci.401.json | 5,3 KB | 50b5cb1ffe14e653eff25e8bf75693f5f38d01313598f51e04825a08a204b48a |
| traces/qrdata_socsci.401.e1.a1.jsonl | 7,8 KB | 52b2bcd4b26e305896dc5f6247b58cf2af690953e0d54f1f8c84a78094ce8e68 |
| trajectories/qrdata_socsci.401.e1.a1.jsonl | 5,4 KB | 267facf1fd7b073b57562aad1c552ff41461aabffb90da6cc855e10bd31859a2 |