{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003567556,0.0002194035,0.0001902665,0.0003637612,0.0003801966,0.0000677065,0.00004970218,0.0001184534,0.0002378273],"category_scores_gemma":[0.00001682475,0.000186837,0.00003957905,0.0004745845,0.00004221343,0.00008991748,0.0000212264,0.0006017582,0.0007092102],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006467044,"about_ca_system_score_gemma":0.000022768,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00039265,"about_ca_topic_score_gemma":0.00009451393,"domain_scores_codex":[0.9986355,0.0001724399,0.0001678782,0.0004853135,0.0001675723,0.000371279],"domain_scores_gemma":[0.9993732,0.0002824625,0.00006238456,0.0001382235,0.00004832528,0.0000954214],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.003599545,0.002443968,0.4280541,0.0001353392,0.0005678866,0.0001359463,0.01355826,0.01210069,0.009092808,0.00495392,0.004823411,0.5205341],"study_design_scores_gemma":[0.01193839,0.004540152,0.8297279,0.0002161271,0.0004963187,0.0004156252,0.002633471,0.1005494,0.00197168,0.00128268,0.04454787,0.001680351],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9756993,0.00007341336,0.005009725,0.0002367765,0.0003639489,0.0002728862,0.00001081391,0.0006803617,0.01765276],"genre_scores_gemma":[0.9899744,0.00002453145,0.0001864064,0.00004657551,0.000139146,0.00008016349,0.0002127523,0.00006005702,0.009275984],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5188538,"threshold_uncertainty_score":0.91157,"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."}}