{"id":"W2244810475","doi":"10.1109/gem.2015.7377246","title":"Course: Rapid advanced multimodal multi-device interactive application prototyping with Max/Jitter, processing, and OpenGL","year":2015,"lang":"en","type":"article","venue":"","topic":"Augmented Reality Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"OpenGL; Computer science; Computer graphics (images); Graphics; Rapid prototyping; Jitter; Presentation (obstetrics); Event (particle physics); Computer graphics; Multimedia; Visualization; Engineering; Artificial intelligence","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":[0.0002523816,0.0001793098,0.0001626602,0.00005828926,0.00015374,0.0001938486,0.0005567818,0.00005430591,0.000003481682],"category_scores_gemma":[0.00002065095,0.0001433835,0.00001530455,0.0003635486,0.00009384703,0.00126381,0.0002383817,0.0001741438,0.00003386632],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008194466,"about_ca_system_score_gemma":0.000171018,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008001513,"about_ca_topic_score_gemma":0.00007483303,"domain_scores_codex":[0.9986666,0.00005001831,0.0002148977,0.0005907268,0.0002433474,0.0002344235],"domain_scores_gemma":[0.9987129,0.00003924827,0.0001905085,0.00052694,0.0003419163,0.0001884731],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008956384,0.0005809724,0.000691388,0.00005615742,0.0000423019,0.000002407982,0.004617868,0.001461425,0.001366233,0.005101989,0.0002542547,0.9857354],"study_design_scores_gemma":[0.002904484,0.000244837,0.003670334,0.0001002827,0.00002009793,0.00005179069,0.001510576,0.973138,0.004828153,0.0002802422,0.0128133,0.0004378914],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004601734,0.0001302293,0.9890361,0.00190716,0.00002508545,0.002238976,0.000002266599,0.0003026466,0.001755765],"genre_scores_gemma":[0.7406542,0.00000791175,0.2569209,0.0004431709,0.0000240781,0.001621999,0.00001092855,0.00001753775,0.0002992612],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9852976,"threshold_uncertainty_score":0.5847008,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02825031292206443,"score_gpt":0.3172009812264545,"score_spread":0.28895066830439,"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."}}