{"id":"W2563833050","doi":"10.5445/ir/1000056619","title":"Story Understanding through Semantic Analysis and Automatic Alignment of Text and Video","year":2016,"lang":"en","type":"article","venue":"Repository KITopen (Karlsruhe Institute of Technology)","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Karlsruhe House of Young Scientists; University of Toronto","keywords":"Computer science; Natural language processing; Artificial intelligence; Semantics (computer science); Information retrieval","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003309955,0.0001701505,0.0005401657,0.0006816489,0.0001915129,0.00005089031,0.0005242164,0.0001668724,0.000003722239],"category_scores_gemma":[0.00007849151,0.0001243755,0.0001069966,0.001196847,0.0004889142,0.0008471445,0.000419629,0.00008525659,0.000001202082],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001182356,"about_ca_system_score_gemma":0.00007122297,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007955619,"about_ca_topic_score_gemma":0.00003880646,"domain_scores_codex":[0.998485,0.00005504902,0.000521993,0.0004601461,0.0002889927,0.0001888214],"domain_scores_gemma":[0.9986277,0.00006634507,0.0004252385,0.0007474329,0.00008310799,0.00005017187],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001953498,0.0002652075,0.1828439,0.0003910954,0.003638443,0.0001049116,0.001138561,0.0002396089,0.2921741,0.4045408,0.000408332,0.1142354],"study_design_scores_gemma":[0.005919793,0.00156229,0.1302861,0.003220815,0.00759764,0.0004603417,0.002177722,0.09507994,0.6602614,0.07785131,0.01254756,0.003035086],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4369258,0.0005558943,0.5597463,0.001548606,0.0001854845,0.000164084,0.000001487483,0.0001099438,0.0007624302],"genre_scores_gemma":[0.982662,0.0002079109,0.01693614,0.00001998988,0.00001250732,0.000009040611,7.246772e-7,0.000006541537,0.0001451191],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5457362,"threshold_uncertainty_score":0.5071884,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01558640996389036,"score_gpt":0.2351384196551752,"score_spread":0.2195520096912848,"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."}}