{"id":"W2032556782","doi":"10.1049/ip-vis:20045109","title":"Techniques for automated reverse storyboarding","year":2005,"lang":"en","type":"article","venue":"IEE Proceedings - Vision Image and Signal Processing","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Storyboard; Pluralistic walkthrough; Computer science; Shot (pellet); Variety (cybernetics); Representation (politics); Artificial intelligence; Motion (physics); Computer vision; Object (grammar); Computer graphics (images); Key (lock); Human–computer interaction; Multimedia; Usability","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":[0.001119914,0.001756164,0.001064628,0.00228357,0.001069201,0.002251525,0.002713391,0.001371295,0.01636242],"category_scores_gemma":[0.004577674,0.001087229,0.001619464,0.00173105,0.001017346,0.002727162,0.003176743,0.0021107,0.01162001],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004032549,"about_ca_system_score_gemma":0.0006045659,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001632196,"about_ca_topic_score_gemma":0.002088151,"domain_scores_codex":[0.9986537,0.0002415904,0.00008717,0.0003372864,0.0005352225,0.0001450955],"domain_scores_gemma":[0.9977061,0.0007795655,0.0001343911,0.0007831027,0.0005287416,0.00006807294],"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":[0.0001834308,0.00008290092,0.0003546335,0.0003504745,0.00004817772,0.0003579409,0.000449509,0.01283517,0.06354594,0.01728551,0.01731173,0.8871946],"study_design_scores_gemma":[0.000131991,0.0002543438,0.001747668,0.000189888,0.00009721061,0.002097841,0.000950851,0.6023054,0.1263313,0.08505717,0.180675,0.0001614603],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001371449,0.0001439848,0.9927347,0.00007026717,0.00003272729,0.00007525378,0.00009833983,0.003853949,0.00161942],"genre_scores_gemma":[0.02862802,0.0003505516,0.966334,0.00005256712,0.00004565256,0.0001575825,0.000678722,0.0006489443,0.00310392],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01636242,"threshold_uncertainty_score":0.05473781,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01152962299780315,"score_gpt":0.3071226133002286,"score_spread":0.2955929903024255,"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."}}