{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001094098,0.0005829047,0.0007052758,0.0005547825,0.0006731905,0.001541003,0.0008220726,0.001033711,0.01318861],"category_scores_gemma":[0.003837918,0.000266312,0.0003592085,0.0009941283,0.0007667047,0.00113208,0.001477143,0.0009061063,0.003920815],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005531784,"about_ca_system_score_gemma":0.0009311829,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003411452,"about_ca_topic_score_gemma":0.0005854177,"domain_scores_codex":[0.9990267,0.0003671186,0.00003799392,0.0001867784,0.0002666256,0.0001148608],"domain_scores_gemma":[0.9990339,0.0003622143,0.00006850919,0.0002647207,0.0002070046,0.00006374807],"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":[0.0007649986,0.0002257589,0.0008578071,0.0001517843,0.0001034375,0.000164608,0.0001062427,0.1402212,0.02638571,0.2920535,0.01149525,0.5274697],"study_design_scores_gemma":[0.00008539966,0.0001732828,0.0007627794,0.00003475432,0.00005861216,0.0004936688,0.00004750397,0.8324259,0.01221468,0.1343558,0.01929616,0.0000514079],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00810071,0.0003308869,0.9656351,0.0002744316,0.0002269574,0.00005453252,0.00003673608,0.0003012274,0.02503949],"genre_scores_gemma":[0.5436231,0.0006587569,0.3914046,0.0007313817,0.0004884471,0.0002207244,0.000158923,0.0001821407,0.06253196],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01318861,"threshold_uncertainty_score":0.04412025,"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."}}