{"id":"W2096217132","doi":"10.1145/2556288.2557304","title":"LACES","year":2014,"lang":"en","type":"preprint","venue":"","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Video production; Computer science; Workflow; Video editing; Casual; Multimedia; Video capture; Status quo; Non-linear editing system; Process (computing); CLIPS; Overhead (engineering); Production (economics); Video processing; Computer graphics (images); Smacker video; Artificial intelligence; Database","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001817915,0.001168828,0.000630223,0.002032643,0.001533108,0.005239359,0.002069658,0.001360509,0.1566888],"category_scores_gemma":[0.006758971,0.0005117799,0.0007202197,0.001261912,0.0008007975,0.00461338,0.003887581,0.001617341,0.1021437],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001041853,"about_ca_system_score_gemma":0.001660943,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002542522,"about_ca_topic_score_gemma":0.003226675,"domain_scores_codex":[0.9980043,0.0002642694,0.0001422854,0.0005266593,0.0008736995,0.0001887525],"domain_scores_gemma":[0.9959748,0.00061937,0.0001811662,0.001238073,0.001470488,0.0005160599],"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":[0.0008598322,0.0002117016,0.001271567,0.0009164487,0.000047876,0.0005656743,0.001679407,0.0009587078,0.02726479,0.06174063,0.4623992,0.442084],"study_design_scores_gemma":[0.00004594035,0.00006160256,0.0005913753,0.000103223,0.00001377318,0.0003332481,0.0002097114,0.002447719,0.00771674,0.005178954,0.9832636,0.00003421787],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.01715536,0.002764219,0.2251378,0.002338942,0.002132864,0.0011121,0.01660854,0.2028964,0.5298537],"genre_scores_gemma":[0.0923279,0.002416769,0.2044277,0.001675014,0.000892688,0.001318973,0.04444998,0.02822433,0.6242666],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.8433112,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01327357366581962,"score_gpt":0.2388602005251291,"score_spread":0.2255866268593095,"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."}}