{"id":"W3038189617","doi":"","title":"Personalized Learning Project: Creating and Playing Matching Games to Encourage Synthesis and Support Consolidation","year":2020,"lang":"en","type":"article","venue":"EdMedia + Innovate Learning","topic":"Educational Games and Gamification","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Consolidation (business); Computer science; Matching (statistics); Multimedia; Human–computer interaction; Business; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007037232,0.0002091118,0.0002751228,0.0002024248,0.0003749439,0.0001664795,0.00009851372,0.0001114821,0.0005612658],"category_scores_gemma":[0.0016471,0.0002207324,0.00002889089,0.0005070803,0.00008339202,0.0001849964,0.00008124997,0.0006144074,0.00009037162],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003274074,"about_ca_system_score_gemma":0.00006712052,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008742938,"about_ca_topic_score_gemma":0.000002348912,"domain_scores_codex":[0.9982489,0.0002472941,0.0003958981,0.0005436018,0.0002221841,0.0003421123],"domain_scores_gemma":[0.9986348,0.0007454667,0.0002598278,0.000100138,0.0001183716,0.0001414249],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.0002831418,0.0000757633,0.2537426,0.0002611116,0.0002089595,0.00004257053,0.5279122,0.0003405007,0.05419609,0.01821839,0.001673322,0.1430453],"study_design_scores_gemma":[0.002895242,0.001105912,0.3861865,0.0007782595,0.0002739988,0.0003232733,0.3400311,0.006027776,0.002581313,0.000574435,0.256863,0.002359136],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9799796,0.0001790159,0.00230356,0.007291445,0.0002240105,0.0003410741,0.000003937797,0.0001813184,0.009496058],"genre_scores_gemma":[0.9927863,0.00004362279,0.003436119,0.0009477444,0.0004395867,0.0001245358,0.00006168155,0.0000471461,0.00211326],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2551897,"threshold_uncertainty_score":0.9001207,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03544606805410062,"score_gpt":0.3236522493638109,"score_spread":0.2882061813097103,"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."}}