{"id":"W2990420886","doi":"10.20380/gi2019.15","title":"VideoWhiz: Non-Linear Interactive Overviews for Recipe Videos","year":2019,"lang":"en","type":"article","venue":"Canada Human-Computer Communications Society","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Recipe; Automatic summarization; Computer science; Workflow; Multimedia; Presentation (obstetrics); Key (lock); Non-linear editing system; World Wide Web; Information retrieval; Human–computer interaction; Artificial intelligence; Video processing; Video tracking; Smacker video","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"],"consensus_categories":[],"category_scores_codex":[0.0004840614,0.0002471605,0.0004012132,0.00006427489,0.0007022462,0.0002266575,0.003440076,0.0001009012,0.00003382024],"category_scores_gemma":[0.00002519293,0.0002511743,0.0003947994,0.0005368036,0.00005995552,0.0006723775,0.001127981,0.0003133521,0.00002432497],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004989698,"about_ca_system_score_gemma":0.0006089042,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.03387225,"about_ca_topic_score_gemma":0.09125149,"domain_scores_codex":[0.9980423,0.0001476972,0.0005823805,0.0005397245,0.000335484,0.000352411],"domain_scores_gemma":[0.9950135,0.0004577239,0.0004279718,0.003493522,0.000478569,0.0001287001],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000155155,0.0006360186,0.006068752,0.0002863293,0.001424471,0.000002888323,0.01198893,0.00810861,0.002754343,0.1412177,0.7595586,0.06793784],"study_design_scores_gemma":[0.0004908706,0.00004699405,0.001777305,0.00005244072,0.00002827217,0.000001956397,0.0001116798,0.7859161,0.0001588109,0.0008457977,0.2102437,0.0003261007],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004274381,0.0002630867,0.987084,0.005303987,0.000590461,0.00080227,0.00002182193,0.0001083804,0.0015516],"genre_scores_gemma":[0.5194625,0.0001425881,0.4692781,0.008414567,0.000230901,0.0001859082,0.0003567279,0.00004197414,0.001886746],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7778075,"threshold_uncertainty_score":0.999994,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02648182364426034,"score_gpt":0.2859164617029527,"score_spread":0.2594346380586923,"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."}}