Tarefa qrdata_socsci.366
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 treatment effect of the growth mindset on the achievement score? Please use propensity score weighting in estimation, use logistic regression to estimate the propensity score, and round the final answer to the nearest hundredth.
Pelas regras deste roteador não identifiquei um desenho que responda à pergunta; nenhuma estimativa foi feita.
Valores esperados e de onde vêm
qrdata.gold.366
- 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 366 (0-based), field 'answer'
- Trecho
item 366 answer 0.39
- Valor
- 0,39
0.39 - 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:
[0.3783,0.4017]· interval · [0.3783, 0.4017] ()
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 | [0.3783,0.4017] | — | interval · [0.3783, 0.4017] () | qrdata.gold.366 | — |
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 treatment effect of the growth mindset on the achievement score? Please use propensity score weighting in estimation, use logistic regression to estimate the propensity score, and round the final answer to the nearest hundredth.
learning_mindset(agent)The National Study of Learning Mindsets is a randomised study conducted in U.S. public high schools which aims at finding the impact of a growth mindset. The way it works is that students receive from the school a seminar to instill in them a growth mindset. Then, they follow up the students in their college years to measure how well they’ve performed academically. This measurement was compiled into an achievement score and standardized. The CSV file learning_mindset.csv contains simulated data of this research. Variable Description intervention: the intervention of the growth mindset; achievement_score: the standardized academic achievement score; schoolid: identifier of the student’s school; success_expect: self-reported expectations for success in the future, a proxy for prior achievement, measured prior to random assignment; ethnicity: categorical variable for student race/ethnicity; gender: categorical variable for student identified gender; frst_in_family: categorical variable for student first-generation status, i.e. first in family to go to college; school_urbanicity: school-level categorical variable for urbanicity of the school, i.e. rural, suburban, etc; school_mindset: school-level mean of students’ fixed mindsets, reported prior to random assignment, standardized; school_achievement: school achievement level, as measured by test scores and college preparation for the previous 4 cohorts of students, standardized; school_ethnic_minority: school racial/ethnic minority composition, i.e., percentage of student body that is Black, Latino, or Native American, standardized; school_poverty: school poverty concentration, i.e., percentage of students who are from families whose incomes fall below the federal poverty line, standardized; school_size: total number of students in all four grade levels in the school, standardized.
file3c3a97cca6fdd96f6856f7856b5f8bc786a31d1a9be78c9f610ef3070783e970· frame — - 1Turno do modelo
call_17c6f83arules-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
9095200bad39ec348171a3af1ded533d7956192392efc9f442557553d2cefe73O 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/0f2bd85f8c198a28c9bbbd7a72fad712916bda948f26e0c035d401c073d3bf8e.json | 273 B | 0f2bd85f8c198a28c9bbbd7a72fad712916bda948f26e0c035d401c073d3bf8e |
| attachments/433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420.json | 2,4 KB | 433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420 |
| attachments/52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b.json | 64,0 KB | 52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b |
| attachments/7412fdda27526a7c90f8832d4e6d6f77b880c6bb52d45d838b2f3abfb7ccf621.json | 2,5 KB | 7412fdda27526a7c90f8832d4e6d6f77b880c6bb52d45d838b2f3abfb7ccf621 |
| native/qrdata_socsci/qrdata_socsci.366.e1.a1.json | 310 B | f9f9e5e903d78b944f02053932e703158840a1ec4b59b91d0848f372f2adca62 |
| sources/qrdata.eval_py.json | 1,3 KB | d610b61f390d29ce767e08ce850eab5cb0dc4d13c1edb62d8cf3b0e49a3a291a |
| sources/qrdata.gold.366.json | 1,4 KB | 0508ddee6cb6c91fff04303df2e88028dd898826249342d7f61560d03aea7670 |
| specs/qrdata_socsci.366.json | 5,3 KB | ba9560f94b3276beda2d7bb07776ed4f536d43a01ab7079b32af2ebbe07800bd |
| traces/qrdata_socsci.366.e1.a1.jsonl | 8,2 KB | 24b78488357e99d646b521db9b34db13981e48074489375ed1d6a949eacec1c8 |
| trajectories/qrdata_socsci.366.e1.a1.jsonl | 5,4 KB | 6fd1d12c74d614715d64286be63f36c5e93606352d0242c80919072cddcb494f |