{"id":"W2245253823","doi":"10.1142/s0219477519500032","title":"Combining Losing Games into a Winning Game","year":2017,"lang":"en","type":"preprint","venue":"Fluctuation and Noise Letters","topic":"Stochastic processes and statistical mechanics","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"HEC Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Random walk; Mathematics; Simple (philosophy); Linear subspace; Simple random sample; Mechanism (biology); Characterization (materials science); Statistical physics; Pure mathematics; Physics; Statistics; Sociology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002340366,0.00137636,0.001487507,0.0006930686,0.001388375,0.003935507,0.002172031,0.001669112,0.009421183],"category_scores_gemma":[0.006849893,0.0006479035,0.001175966,0.0005927043,0.002550231,0.005835264,0.004063407,0.003226644,0.001111834],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006957166,"about_ca_system_score_gemma":0.00106985,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006199486,"about_ca_topic_score_gemma":0.0007706819,"domain_scores_codex":[0.9977285,0.0009405451,0.0001344363,0.0004146198,0.0004576141,0.0003243528],"domain_scores_gemma":[0.9973288,0.001444526,0.000176249,0.0003274218,0.0002228676,0.0005002175],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001747488,0.0001300861,0.000519531,0.00008252064,0.00009051761,0.0001765792,0.0003294953,0.02782885,0.003490742,0.9500234,0.002054096,0.01509941],"study_design_scores_gemma":[0.00002020506,0.00006836143,0.0001047569,0.000009351776,0.00003018923,0.00004645685,0.00007636923,0.1091436,0.0006267974,0.8883201,0.00153993,0.00001374784],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1156959,0.0001457734,0.8350918,0.001167701,0.0002263419,0.0001325897,0.0001023863,0.0003230651,0.04711458],"genre_scores_gemma":[0.8716925,0.0001446564,0.09407717,0.0003641391,0.0001528596,0.0001589534,0.0001352882,0.0002775611,0.03299687],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009421183,"threshold_uncertainty_score":0.03151697,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05122884638565692,"score_gpt":0.3336816468537752,"score_spread":0.2824528004681183,"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."}}