{"id":"W2913694129","doi":"10.1145/3279952","title":"Deep Learning–Based Multimedia Analytics","year":2019,"lang":"en","type":"article","venue":"ACM Transactions on Multimedia Computing Communications and Applications","topic":"Multimodal Machine Learning Applications","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"National Laboratory of Pattern Recognition; National Natural Science Foundation of China","keywords":"Computer science; Deep learning; Analytics; Closed captioning; Multimedia; Milestone; Domain (mathematical analysis); Visual analytics; Learning analytics; Data science; Artificial intelligence; Visualization; Image (mathematics)","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":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0004626168,0.0003706118,0.0003559055,0.0004529027,0.001328339,0.0002578462,0.003593636,0.0001801431,0.0000619888],"category_scores_gemma":[0.00009537709,0.0003998824,0.0001640672,0.001341559,0.0002667877,0.0002594806,0.0002541152,0.001184467,0.000748347],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000099818,"about_ca_system_score_gemma":0.00009968692,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001406591,"about_ca_topic_score_gemma":0.00002462712,"domain_scores_codex":[0.9974463,0.0002725172,0.000599344,0.000854557,0.0003655383,0.0004617415],"domain_scores_gemma":[0.9909934,0.002873891,0.0002993794,0.005269526,0.0002709331,0.0002928636],"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":[0.000006499537,0.0007603,0.003943854,0.00003545449,0.00007514754,3.957905e-7,0.000649704,0.1043763,0.0007644807,0.005127624,0.00001623584,0.884244],"study_design_scores_gemma":[0.0007761925,0.00009051563,0.007726938,0.00003325712,0.00003946724,0.000007328463,0.00009394209,0.9766108,0.0001621681,0.0008098455,0.01324126,0.000408332],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003130428,0.0001997124,0.9885811,0.004754425,0.00008974052,0.001219171,0.0000107615,0.0009115083,0.00110318],"genre_scores_gemma":[0.5818354,0.0001235041,0.4172176,0.0002832805,0.00002983815,0.0002891473,0.00005566502,0.00003095936,0.0001346743],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8838357,"threshold_uncertainty_score":0.9999718,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01748353478677071,"score_gpt":0.2872168292985619,"score_spread":0.2697332945117912,"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."}}