Tarefa qrdata_socsci.380
qrdata_socsciagentT2publicépoca 1, tentativa 1Execução: 20261015T170010Z-suite-qrdata_socsci-scripted-7912b3Veredito
Família do método (das chamadas de ferramentas): ols (regression)
Pergunta feita ao analista
What is the difference in the proportion of full-time employees in NJ and PA? Please round to the nearest thousandth.
Estimativa (infer.ols): coef.nj = 0,04812. Caminho de referência inferido à mão.
Valores esperados e de onde vêm
qrdata.gold.380
- 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 380 (0-based), field 'answer'
- Trecho
item 380 answer 0.048
- Valor
- 0,048
0.048 - 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:
[0.04656,0.049440000000000005]· interval · [0.04656, 0.049440000000000005] ()
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 0.048 in (0.04656, 0.04944)? | obrigatória | passou | [0.04656,0.049440000000000005] | 0,0480.048 | interval · [0.04656, 0.049440000000000005] () | qrdata.gold.380 | call_0fcf28c0 |
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 | |
|---|---|---|---|
| cluster | — | — | não fixado pela referência |
| controls | — | [] | não fixado pela referência |
| dataset | "minwage_props" | "minwage_props" | igual |
| fe | — | [] | não fixado pela referência |
| intercept | — | true | não fixado pela referência |
| level | — | 0,950.95 | não fixado pela referência |
| se | — | "HC1" | não fixado pela referência |
| subset | — | — | não fixado pela referência |
| weights | — | — | não fixado pela referência |
| x | ["nj"] | ["nj"] | igual |
| y | "fullPropAfter" | "fullPropAfter" | igual |
Trajetória
Cada evento na ordem em que aconteceu: turnos do modelo, chamadas de ferramentas, guardas, transformações e pontuação.
- 0Início
What is the difference in the proportion of full-time employees in NJ and PA? Please round to the nearest thousandth.
minwage(agent)Two social science researchers examined the impact of raising the minimum wage on employment in the fast-food industry. In 1992, the state of New Jersey (NJ) in the United States raised the minimum wage from $4.25 to $5.05 per hour. Did such an increase in the minimum wage reduce employment as economic theory predicts? As discussed above, answering this question requires inference about the NJ employment rate in the absence of such a raise in the minimum wage. Since this counterfactual outcome is not observable, we must somehow estimate it using observed data. One possible strategy is to look at another state in which the minimum wage did not increase. For example, the researchers of this study chose the neighboring state, Pennsylvania (PA), on the grounds that NJ’s economy resembles that of Pennsylvania, and hence the fast-food restaurants in the two states are comparable. Under this cross-section comparison design, therefore, the fast-food restaurants in NJ serve as the treatment group receiving the treatment (i.e., the increase in the minimum wage), whereas those in PA represent the control group, which did not receive such a treatment. To collect pretreatment and outcome measures, the researchers surveyed the fast-food restaurants before and after the minimum wage increase. Specifically, they gathered information about the number of full-time employees, the number of part-time employees, and their hourly wages, for each restaurant. The CSV file minwage.csv contains this data set. Variable Description chain: name of the fast-food restaurant chain location: location of the restaurants (centralNJ, northNJ, PA, shoreNJ, southNJ) wageBefore: wage before the minimum-wage increase wageAfter: wage after the minimum-wage increase fullBefore: number of full-time employees before the minimum-wage increase fullAfter: number of full-time employees after the minimum-wage increase partBefore: number of part-time employees before the minimum-wage increase partAfter: number of part-time employees after the minimum-wage increase
file3c3a97cca6fdd96f6856f7856b5f8bc786a31d1a9be78c9f610ef3070783e970· frame —minwage_props(reference)file3c3a97cca6fdd96f6856f7856b5f8bc786a31d1a9be78c9f610ef3070783e970· frame —derive{"args":{"columns":["fullAfter","partAfter"]},"name":"totalAfter","op":"sum"}derive{"args":{"left":"fullAfter","right":"totalAfter"},"name":"fullPropAfter","op":"ratio"}derive{"args":{"columns":["fullBefore","partBefore"]},"name":"totalBefore","op":"sum"}derive{"args":{"left":"fullBefore","right":"totalBefore"},"name":"fullPropBefore","op":"ratio"}derive{"args":{"left":"fullPropAfter","right":"fullPropBefore"},"name":"fullPropChange","op":"diff"}derive{"args":{"cmp":"ne","column":"location","value":"PA"},"name":"nj","op":"indicator"}
- 1Transformação
minwage_props·derive{"args":{"columns":["fullAfter","partAfter"]},"name":"totalAfter","op":"sum"}358 → 358 linhasFrame antes → depois:55dec890a044→808a12f40fca - 2Transformação
minwage_props·derive{"args":{"left":"fullAfter","right":"totalAfter"},"name":"fullPropAfter","op":"ratio"}358 → 358 linhasFrame antes → depois:808a12f40fca→891fe31e6dd3 - 3Transformação
minwage_props·derive{"args":{"columns":["fullBefore","partBefore"]},"name":"totalBefore","op":"sum"}358 → 358 linhasFrame antes → depois:891fe31e6dd3→fc02f54744c1 - 4Transformação
minwage_props·derive{"args":{"left":"fullBefore","right":"totalBefore"},"name":"fullPropBefore","op":"ratio"}358 → 358 linhasFrame antes → depois:fc02f54744c1→590bda545f53 - 5Transformação
minwage_props·derive{"args":{"left":"fullPropAfter","right":"fullPropBefore"},"name":"fullPropChange","op":"diff"}358 → 358 linhasFrame antes → depois:590bda545f53→3ab27676376a - 6Transformação
minwage_props·derive{"args":{"cmp":"ne","column":"location","value":"PA"},"name":"nj","op":"indicator"}358 → 358 linhasFrame antes → depois:3ab27676376a→67fcfadd313b - 7Turno do modelo
call_f8eb79afreferenceFim:tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cacheTexto visívelOLS of the after-increase proportion of full-time employees on the NJ indicator: NJ mean minus PA mean (QSS 2.5.1).
Chamadas pedidasinfer.ols{ "dataset": "minwage_props", "x": [ "nj" ], "y": "fullPropAfter" }
- 8Ferramenta
infer.olsv0.1.0call_0fcf28c0decidido pelo modeloreexecutávelMotivo: OLS of the after-increase proportion of full-time employees on the NJ indicator: NJ mean minus PA mean (QSS 2.5.1).
Argumentos
{ "cluster": null, "controls": [], "dataset": "minwage_props", "fe": [], "intercept": true, "level": 0.95, "se": "HC1", "subset": null, "weights": null, "x": [ "nj" ], "y": "fullPropAfter" }Figuras call_0fcf28c0 Chave Valor Valor bruto Erro-padrão IC coef.nj Coefficient on nj in the regression of fullPropAfter0,0481189 0.048118861422914130,0334779 0.03347789248583884-0,0177204 – 0,113958 Diagnósticosn_obs= 358358k= 22r2= 0,005621660.005621660177508314df_resid= 356356se_type="HC1"n_clusters= —
Tabelas (1)
{ "coefficients": [ { "ci_high": 0.33140603923428874, "ci_low": 0.2131581844819965, "coef": 0.2722821118581426, "p": 9.019998230055255e-18, "se": 0.030063268766730506, "t": 9.05696961866846, "term": "Intercept" }, { "ci_high": 0.11395815851989526, "ci_low": -0.01772043567406699, "coef": 0.04811886142291413, "p": 0.1515018869658016, "se": 0.03347789248583884, "t": 1.437332455836891, "term": "nj" } ] }- Digests dos dados
minwage_props67fcfadd313ba1d19f472b3dfda49f69f3c7884474526b8969637c189accc2da- sha256 do resultado
c7d67b09c8372523a36282c9b3526d7a9cccff18b9ab5a4ad0159d13618ac47a
- 9Turno do modelo
call_2c7c99e1referenceFim:tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cacheChamadas pedidassubmit_answer(meta-ferramenta){ "answer": "Estimativa (infer.ols): coef.nj = 0,04812. Caminho de referência inferido à mão.", "caveats": [], "figure_refs": [ "call_0fcf28c0:coef.nj" ] }
- 10Guardaresposta aceita
- Números encontrados
- 0,04812
- Sem lastro
- —
- Não citados
- —
1 nova(s) tentativa(s) restante(s) - 11Resposta
Estimativa (infer.ols): coef.nj = 0,04812. Caminho de referência inferido à mão.
Figuras citadas:call_0fcf28c0:coef.nj· Parada:answeredFigura da resposta:call_0fcf28c0:coef.nj= 0,04811890.04811886142291413(se 0,03347790.03347789248583884) - 12Pontuação#0resposta com lastropassouobrigatória
every number backed - 13Pontuação#1figura da resposta próximapassouobrigatória
0.048 in (0.04656, 0.04944)?
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
7880b8b96ba1d9d2e308c2bbd15291039c409f4bb2a0138254684fc05486289dO 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/26fd7a0d94fd7da889ec8651abf11b28d4dae3c46cfe05eb87302d4895c1a351.json | 3,1 KB | 26fd7a0d94fd7da889ec8651abf11b28d4dae3c46cfe05eb87302d4895c1a351 |
| attachments/3ab65e3907c133e9f4997aaf9ccdf83f93e31f6729c68ed30ca8d0098db86531.json | 264 B | 3ab65e3907c133e9f4997aaf9ccdf83f93e31f6729c68ed30ca8d0098db86531 |
| attachments/433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420.json | 2,4 KB | 433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420 |
| attachments/52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b.json | 64,0 KB | 52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b |
| attachments/823ba6a227f2fe72f14fd1fb5ec865781efa01b98827ee46bcdc81fc50186af5.json | 867 B | 823ba6a227f2fe72f14fd1fb5ec865781efa01b98827ee46bcdc81fc50186af5 |
| attachments/84e63c8cd555d9684acd5af604dbff7c69b49bf1f1afe94e874adf05d8eb6e46.json | 973 B | 84e63c8cd555d9684acd5af604dbff7c69b49bf1f1afe94e874adf05d8eb6e46 |
| attachments/bb7ec3f1839f6e5f6a4b0c977bd160dc725cbe0bb175c49a844bec16b9b2a9d7.json | 142 B | bb7ec3f1839f6e5f6a4b0c977bd160dc725cbe0bb175c49a844bec16b9b2a9d7 |
| native/qrdata_socsci/qrdata_socsci.380.e1.a1.json | 453 B | 04b57ab29c9f21df0b1bd781486d1e84c409b1846fc2d86cb33ddc07a7f8b8a0 |
| sources/qrdata.eval_py.json | 1,3 KB | d610b61f390d29ce767e08ce850eab5cb0dc4d13c1edb62d8cf3b0e49a3a291a |
| sources/qrdata.gold.380.json | 1,4 KB | 3418c02ab8223944b9b127ac1aaa7569ea135b00f1e900882907ae81dd71c191 |
| specs/qrdata_socsci.380.json | 6,5 KB | 00b14dc81c22481707b3f348425fa361af818f8ac36b402ea2dd3ca6b7c93bc3 |
| traces/qrdata_socsci.380.e1.a1.jsonl | 19,5 KB | df572d11a23cb60790b1bc0936c212e7cba029f53eca6207204801988d00bae5 |
| trajectories/qrdata_socsci.380.e1.a1.jsonl | 14,8 KB | 777860bb200967defec37a2e16ba4466d398db9c02646335b502a6ca9bf61f00 |