{"id":"W4390496627","doi":"10.17504/protocols.io.kxygx34jdg8j/v1","title":"[PEER REVIEWED] Generating Non-English IATs and Collecting Offline Samples v1","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Flexibility (engineering); Psychology; Protocol (science); Computer science; Cognitive psychology; Mathematics; Statistics; Medicine","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.008986074,0.001441324,0.001348681,0.002483243,0.001819902,0.003968415,0.001963913,0.001535095,0.4462767],"category_scores_gemma":[0.05411083,0.001060804,0.0006466158,0.002499193,0.0008260428,0.002784416,0.004897496,0.001912694,0.4468858],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007084602,"about_ca_system_score_gemma":0.004027603,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002186822,"about_ca_topic_score_gemma":0.003371248,"domain_scores_codex":[0.9922559,0.002704956,0.0008607818,0.001308882,0.002374599,0.0004949302],"domain_scores_gemma":[0.9493419,0.01081687,0.001096543,0.01977442,0.0175754,0.001394944],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006123608,0.0001158045,0.001841464,0.000521346,0.00003449311,0.0001980662,0.0005108638,0.0005214296,0.005114481,0.01041528,0.8792298,0.1008847],"study_design_scores_gemma":[0.0001737825,0.00009982994,0.002290436,0.0001785213,0.00001866815,0.0002114045,0.0003706425,0.003653538,0.007278318,0.0100083,0.9756168,0.00009962451],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"methods","genre_scores_codex":[0.01241179,0.0003632385,0.2136217,0.003357068,0.004086348,0.01277219,0.3506761,0.1668508,0.2358608],"genre_scores_gemma":[0.07328533,0.0005426227,0.1975778,0.002481264,0.00147471,0.0193542,0.3786209,0.06124291,0.2654202],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4462767,"threshold_uncertainty_score":0.7898191,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08980624709348774,"score_gpt":0.313647439964621,"score_spread":0.2238411928711333,"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."}}