{"id":"W4377078544","doi":"10.2139/ssrn.4448249","title":"Adaptive Neyman Allocation","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Advanced Bandit Algorithms Research","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Quest University Canada","funders":"","keywords":"Computer science; Mathematics; Econometrics; Statistics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.01036521,0.0001236559,0.0001840741,0.0006476344,0.0004141883,0.0002051133,0.0009756348,0.00006471057,0.0001521092],"category_scores_gemma":[0.002025736,0.00009113186,0.000124026,0.002182015,0.0000862416,0.0005965386,0.0001305313,0.001710352,0.00365711],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007932318,"about_ca_system_score_gemma":0.00207363,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001843555,"about_ca_topic_score_gemma":0.0003540909,"domain_scores_codex":[0.9943011,0.0002522166,0.0004469143,0.0003473454,0.00205979,0.002592645],"domain_scores_gemma":[0.9982333,0.0006020307,0.0001844495,0.0003479228,0.00048645,0.0001458153],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001312439,0.00005282435,0.0007846774,8.675151e-7,0.000108219,0.00003794818,0.0004831006,0.006781128,0.0004943067,0.0939934,0.007305149,0.8898271],"study_design_scores_gemma":[0.0003858673,0.0002844246,0.002105315,0.000004380433,0.000004188337,0.0001751988,0.005739389,0.01284157,0.0001398004,0.969091,0.009101856,0.0001270263],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1043137,0.001480801,0.8803779,0.008522964,0.0009795005,0.0003887697,0.000007235223,0.0002516111,0.003677496],"genre_scores_gemma":[0.9713864,0.001364095,0.0002520657,0.0000537275,0.0004725656,0.000009443395,0.000003274354,0.00002256798,0.02643588],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8897001,"threshold_uncertainty_score":0.9971187,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09883633605555571,"score_gpt":0.4255126959982633,"score_spread":0.3266763599427076,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}