{"id":"W2958239831","doi":"10.1109/isbi.2019.8759511","title":"Real-Time Informative Laryngoscopic Frame Classification with Pre-Trained Convolutional Neural Networks","year":2019,"lang":"en","type":"article","venue":"","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Initialization; Convolutional neural network; Artificial intelligence; Task (project management); Inference; Frame (networking); Process (computing); Recurrent neural network; Contextual image classification; Artificial neural network; Machine learning; Pattern recognition (psychology); 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":[],"consensus_categories":[],"category_scores_codex":[0.0001165045,0.0001170408,0.0001252013,0.00005625814,0.00009045899,0.0001784642,0.0003896059,0.00006284819,0.0001193477],"category_scores_gemma":[0.000008154779,0.00008578202,0.00002494561,0.0003112011,0.000044366,0.00129624,0.00007305115,0.0001315623,0.000172156],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003799199,"about_ca_system_score_gemma":0.00009614972,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000130133,"about_ca_topic_score_gemma":0.000002438848,"domain_scores_codex":[0.9990813,0.00002516781,0.0001745852,0.0002272463,0.0002372778,0.0002544753],"domain_scores_gemma":[0.9993494,0.00006418931,0.0001160388,0.0002884572,0.0001072148,0.00007475441],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007624953,0.0009002342,0.2195814,0.0004920269,0.0005524314,0.00004273986,0.01470876,0.09151389,0.1268679,0.2691675,0.02552552,0.2498851],"study_design_scores_gemma":[0.0004368897,0.000144104,0.08143054,0.00002684987,0.000002790487,0.00001360174,0.00003526678,0.9156724,0.001598167,0.0003075787,0.0001741783,0.0001576769],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3342003,0.00002292123,0.6378636,0.0009481409,0.000131677,0.0002778541,0.000001007493,0.0003700224,0.0261845],"genre_scores_gemma":[0.9417143,0.000004887359,0.05561089,0.0005707552,0.00005360273,0.00001183415,0.00001689448,0.000006063604,0.002010734],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8241585,"threshold_uncertainty_score":0.3498089,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007010017683578616,"score_gpt":0.2228203561833392,"score_spread":0.2158103384997606,"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."}}