{"id":"W4366390417","doi":"10.3390/make5020024","title":"Lottery Ticket Search on Untrained Models with Applied Lottery Sample Selection","year":2023,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Gambling Behavior and Treatments","field":"Psychology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Lottery; Computer science; Ticket; Fraction (chemistry); Machine learning; Sample (material); Set (abstract data type); Selection (genetic algorithm); Artificial intelligence; Artificial neural network; Process (computing); Mathematics; Statistics; Computer security","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":[0.002942476,0.001114818,0.0018033,0.001638517,0.0006070461,0.001475312,0.002128122,0.00151727,0.004079306],"category_scores_gemma":[0.01093251,0.0006499382,0.001341463,0.001244137,0.0007463674,0.002937622,0.001669238,0.001755829,0.0007481881],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00124466,"about_ca_system_score_gemma":0.001387383,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005004478,"about_ca_topic_score_gemma":0.009167262,"domain_scores_codex":[0.9990259,0.0004814,0.00007481707,0.0002125683,0.0001167259,0.00008864503],"domain_scores_gemma":[0.9943541,0.004056579,0.0002885144,0.0007986705,0.0003851663,0.0001169349],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004942942,0.0003490573,0.008041674,0.000203512,0.0001792638,0.0001416543,0.0001657232,0.8175246,0.001422287,0.005556208,0.002854313,0.1630675],"study_design_scores_gemma":[0.000020157,0.0000579461,0.0003189599,0.00001386387,0.00001019007,0.00001340884,0.00002581023,0.9947624,0.0003845645,0.004098306,0.0002880518,0.000006440206],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3249221,0.001249844,0.6622949,0.00123872,0.0001278783,0.0003725972,0.001309191,0.003950629,0.004534098],"genre_scores_gemma":[0.8045709,0.0001880985,0.1899,0.0004450426,0.0000431461,0.0002726837,0.002039108,0.0001617333,0.002379313],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005004478,"threshold_uncertainty_score":0.01556146,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08505546188253313,"score_gpt":0.3895661560022754,"score_spread":0.3045106941197423,"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."}}