{"id":"W2169266007","doi":"10.1109/ism.2006.45","title":"Chaotic Synchronization of MPEG-7 Descriptors for Interpretation in Surveillance Video","year":2006,"lang":"en","type":"article","venue":"","topic":"Fractal and DNA sequence analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Feature (linguistics); Synchronization (alternating current); Segmentation; Frame (networking); Artificial intelligence; Computer vision; Chaotic; Semantic feature; Object (grammar); Video tracking; Semantics (computer science); Semantic analysis (machine learning); Feature vector; Pattern recognition (psychology)","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":[],"consensus_categories":[],"category_scores_codex":[0.00008478865,0.00005144054,0.00008024996,0.00004164677,0.000010812,0.000005223978,0.0000483291,0.00003968482,0.00001090318],"category_scores_gemma":[0.00005734734,0.00004663808,0.00004574106,0.00009815756,0.00002363774,0.000003864177,0.00001051134,0.00001248137,0.00000117856],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001136316,"about_ca_system_score_gemma":0.00001789309,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003306617,"about_ca_topic_score_gemma":0.001240849,"domain_scores_codex":[0.9995775,0.00001482357,0.000158921,0.0001270443,0.00004166335,0.00008003769],"domain_scores_gemma":[0.9997764,0.000008788043,0.00005204317,0.0000862574,0.00006682002,0.000009644597],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001002445,0.00008014161,0.09361491,0.00005375055,0.00002196578,5.000844e-7,0.00002841192,0.005348895,0.8947003,0.0005850727,0.0006001646,0.004865618],"study_design_scores_gemma":[0.001143098,0.0004900091,0.06283036,0.0000434412,0.0000274683,0.000003549769,0.0002051929,0.06044899,0.8696577,0.002352366,0.002413212,0.0003845933],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8949359,0.0002327979,0.1038985,0.0000610215,0.00003671813,0.00009496033,0.000005195365,0.000004092937,0.0007307746],"genre_scores_gemma":[0.9985052,0.00001999003,0.0007869361,0.00004388742,0.00003739692,0.00001161916,0.0002068191,0.00000528336,0.000382882],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1035693,"threshold_uncertainty_score":0.1901846,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004265997015052848,"score_gpt":0.219190436113201,"score_spread":0.2149244390981481,"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."}}