{"id":"W4386629052","doi":"10.33093/jiwe.2023.2.2.6","title":"AIRA: An Intelligent Recommendation Agent Application for Movies","year":2023,"lang":"en","type":"article","venue":"Journal of Informatics and Web Engineering","topic":"Child Development and Digital Technology","field":"Social Sciences","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Drama; Limiting; Computer science; Psychology; Content (measure theory); Multimedia; Engineering","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":[0.0004454803,0.00003536974,0.00006789657,0.0001588806,0.0000663474,0.00004584681,0.0000616541,0.00003343159,0.00000265311],"category_scores_gemma":[0.00008748823,0.00003159174,0.00002108894,0.0001349958,0.00001075161,0.000335244,0.00001538285,0.0000442611,0.00000244788],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002935234,"about_ca_system_score_gemma":0.00003319751,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001653884,"about_ca_topic_score_gemma":0.000006302394,"domain_scores_codex":[0.9995685,0.000002160488,0.0002537068,0.00001794879,0.00007351959,0.00008415299],"domain_scores_gemma":[0.9997217,0.00004119116,0.0001080252,0.00002546918,0.0000608561,0.00004275073],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001136157,0.0000233367,0.0007398569,0.00009908666,0.00003774818,5.175048e-7,0.01571816,0.004017799,0.0002078817,0.09132417,0.001386127,0.886434],"study_design_scores_gemma":[0.0003241153,0.0001210301,0.001006174,0.00004599514,0.00001108012,0.000006504431,0.01135116,0.1537482,0.0004632433,0.003156649,0.8296137,0.0001520668],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7374094,0.00005868991,0.2570384,0.001561611,0.0005434214,0.0003130856,0.000005611212,0.0001435782,0.002926166],"genre_scores_gemma":[0.9913287,0.0007420971,0.007700324,0.00005124084,0.0001052992,0.00000603414,0.00001094659,0.000004614269,0.00005074208],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8862819,"threshold_uncertainty_score":0.1288274,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02233319042885703,"score_gpt":0.2887973887008897,"score_spread":0.2664641982720327,"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."}}