Tarefa qrdata_socsci.370
qrdata_socsciagentT2publicépoca 1, tentativa 1Execução: 20261015T170010Z-suite-qrdata_socsci-scripted-7912b3Veredito
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.
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
- 0,1
0.1 - 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 conferidoThe 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
| # | Verificação | Obrigatória | Resultado | Esperado | Observado | Tolerância | Fonte | Chamada |
|---|---|---|---|---|---|---|---|---|
| 0 | resposta com lastro every number backed | obrigatória | passou | — | [] | — | — | — |
| 1 | figura da resposta próxima 0.1 in (0.097, 0.103)? | obrigatória | passou | [0.097,0.10300000000000001] | 0,10.1 | interval · [0.097, 0.10300000000000001] () | qrdata.gold.370 | call_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
| Argumento | Referência | Agente | |
|---|---|---|---|
| bandwidth | 11 | 11 | igual |
| cluster | — | — | não fixado pela referência |
| cutoff | 2121 | 2121 | igual |
| dataset | "drinking_log" | "drinking_log" | igual |
| kernel | "uniform" | "uniform" | igual |
| level | — | 0,950.95 | não fixado pela referência |
| polynomial | 11 | 11 | igual |
| 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.
- 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).
file3c3a97cca6fdd96f6856f7856b5f8bc786a31d1a9be78c9f610ef3070783e970· frame —drinking_log(reference)file3c3a97cca6fdd96f6856f7856b5f8bc786a31d1a9be78c9f610ef3070783e970· frame —derive{"args":{"column":"all"},"name":"log_all","op":"log"}
- 1Transformação
drinking_log·derive{"args":{"column":"all"},"name":"log_all","op":"log"}50 → 50 linhasFrame antes → depois:d60f201e1a38→4b7a4f0300d4 - 2Turno do modelo
call_efaeb706referenceFim:tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cacheTexto visívelSharp 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 pedidasinfer.rdd{ "bandwidth": 1, "cutoff": 21, "dataset": "drinking_log", "kernel": "uniform", "polynomial": 1, "running": "agecell", "y": "log_all" }
- 3Ferramenta
infer.rddv0.1.0call_3cbe569adecidido pelo modeloreexecutávelMotivo: 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 Chave Valor Valor bruto Erro-padrão IC effect Jump in log_all at the cutoff of agecell0,0997942 0.099794201007424660,0190967 0.0190966775064627360,0623654 – 0,137223 Diagnósticosbandwidth= 11bandwidth_method="user"n_left= 1212n_right= 1212kernel="uniform"polynomial= 11bandwidth_sensitive=falsen_obs= 4848se_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_log4b7a4f0300d4de0e81e79acff3018fe712690c9427eaadc45ae7245194f8f2d3- sha256 do resultado
5338fb344e6700a817569fcc0f7bcca254d5129bb6c0b271bb1d3c037ea3d260
- 4Turno do modelo
call_3467a1e6referenceFim:tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cacheChamadas pedidassubmit_answer(meta-ferramenta){ "answer": "Estimativa (infer.rdd): effect = 0,09979. Caminho de referência inferido à mão.", "caveats": [], "figure_refs": [ "call_3cbe569a:effect" ] }
- 5Guardaresposta aceita
- Números encontrados
- 0,09979
- Sem lastro
- —
- Não citados
- —
1 nova(s) tentativa(s) restante(s) - 6Resposta
Estimativa (infer.rdd): effect = 0,09979. Caminho de referência inferido à mão.
Figuras citadas:call_3cbe569a:effect· Parada:answeredFigura da resposta:call_3cbe569a:effect= 0,09979420.09979420100742466(se 0,01909670.019096677506462736) - 7Pontuação#0resposta com lastropassouobrigatória
every number backed - 8Pontuação#1figura da resposta próximapassouobrigatória
0.1 in (0.097, 0.103)?
Épocas × verificações
| # | 0 | 1 |
|---|---|---|
| Época 1 | passou | passou |
Tentativas
Cada tentativa é seu próprio arquivo e é publicada; só a última de cada época é pontuada.
- época 1, tentativa 1passoupontuada(exibindo)
Downloads desta tarefa
9d947bb4060a378f75d83a4a1f7bdd7e1b5a11d90720dbaa30e91113c5ee3754O 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.
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| attachments/e2a2d65c832140472ef2ead193347aadfbd12d7804d21c4b1199d9f00275f370.json | 393 B | e2a2d65c832140472ef2ead193347aadfbd12d7804d21c4b1199d9f00275f370 |
| native/qrdata_socsci/qrdata_socsci.370.e1.a1.json | 446 B | aa858c7ac791695bc3c3e9543778bd7c55ae4007624fbb1c926e40864d6b39ce |
| sources/qrdata.eval_py.json | 1,3 KB | d610b61f390d29ce767e08ce850eab5cb0dc4d13c1edb62d8cf3b0e49a3a291a |
| sources/qrdata.gold.370.json | 1,7 KB | 8ce23493966e2d038820ee0320d1e502f99bfbc9440345f46205162c0c2567ae |
| specs/qrdata_socsci.370.json | 5,2 KB | d6e6e6cdd51069e226823fb421337f91d710eb10c730608ff2f1475121d6869f |
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