{"id":"W4387124582","doi":"10.1016/j.dib.2023.109627","title":"CineScale2: a dataset of cinematic camera features in movies","year":2023,"lang":"en","type":"article","venue":"Data in Brief","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Surgical Specialties (Canada)","funders":"","keywords":"Computer science; Artificial intelligence; Convolutional neural network; Computer vision; Shot (pellet); Video camera; Frame (networking); Feature (linguistics); Camera auto-calibration; Smart camera; Orientation (vector space); Computer graphics (images); Camera resectioning","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003356778,0.002306921,0.0009455466,0.004423535,0.0007049412,0.00110584,0.001325804,0.001561194,0.01117442],"category_scores_gemma":[0.001790108,0.0003740143,0.0007813057,0.003610729,0.0003157357,0.001097902,0.001114651,0.001001808,0.01393623],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008400733,"about_ca_system_score_gemma":0.000633991,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01448283,"about_ca_topic_score_gemma":0.04614152,"domain_scores_codex":[0.9993579,0.00006493283,0.00007050819,0.0002234355,0.0001871806,0.00009606587],"domain_scores_gemma":[0.9991512,0.0001533169,0.0001194576,0.0002038425,0.0002474399,0.0001247031],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00061335,0.000431807,0.007781972,0.002775467,0.0001386501,0.0007129316,0.0003812081,0.001121779,0.01057147,0.0009928719,0.8930504,0.08142804],"study_design_scores_gemma":[0.0002151918,0.0002919889,0.1148912,0.0008612658,0.0001572531,0.002023729,0.001250446,0.01072683,0.0120622,0.0016045,0.8557407,0.0001746953],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.02326011,0.001807823,0.002977271,0.0001850522,0.0002393895,0.0002549384,0.9619998,0.003681909,0.005593753],"genre_scores_gemma":[0.01407586,0.0003528709,0.004847625,0.00005312339,0.00004788052,0.000200656,0.9786257,0.0001330304,0.001663284],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01448283,"threshold_uncertainty_score":0.03738213,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03061943435541232,"score_gpt":0.2980896555910983,"score_spread":0.267470221235686,"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."}}