{"id":"W4319081304","doi":"10.21203/rs.3.rs-2505522/v1","title":"Lottery Ticket Search on Untrained Models With Applied Lottery Sample Selection","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Gambling Behavior and Treatments","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Lottery; Computer science; Ticket; Fraction (chemistry); Sample (material); Set (abstract data type); Machine learning; Selection (genetic algorithm); Artificial intelligence; Artificial neural network; Process (computing); Mathematics; Statistics; Computer security","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":[0.002824623,0.001140222,0.002022382,0.0009326253,0.000442076,0.0009671035,0.001846426,0.001787008,0.003249279],"category_scores_gemma":[0.009714373,0.0006536112,0.00114586,0.0007570866,0.0009282809,0.001836269,0.001170318,0.00202662,0.0004758913],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001189543,"about_ca_system_score_gemma":0.0010058,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00673773,"about_ca_topic_score_gemma":0.007186064,"domain_scores_codex":[0.9990803,0.0005642513,0.00004607132,0.0001453282,0.00006912043,0.00009495515],"domain_scores_gemma":[0.9937462,0.00503788,0.0002636996,0.000501081,0.0002878421,0.000163248],"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":[0.0005192797,0.0002848621,0.003334788,0.00009999982,0.0001450855,0.00006667881,0.00007541913,0.9428697,0.0005236244,0.002588154,0.001158244,0.04833421],"study_design_scores_gemma":[0.00002047632,0.0000526146,0.0001620094,0.000006698146,0.000007893157,0.000005556926,0.00001053037,0.9978902,0.00015186,0.001625758,0.00006313975,0.000003313019],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6257626,0.001947334,0.3634141,0.001584932,0.0001516015,0.0002422813,0.0005016872,0.002250543,0.004144819],"genre_scores_gemma":[0.9373251,0.0001368926,0.05959219,0.0002905743,0.00003559927,0.0001070969,0.0005000919,0.00008827777,0.001924124],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00673773,"threshold_uncertainty_score":0.01493818,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4265927110154343,"score_gpt":0.5027315800932756,"score_spread":0.07613886907784129,"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."}}