{"id":"W2536188624","doi":"10.1109/icecs.2004.1399738","title":"VLSI sensor for multiple targets detection and tracking","year":2005,"lang":"en","type":"article","venue":"","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Snapshot (computer storage); Computer vision; Very-large-scale integration; Artificial intelligence; Object detection; Video tracking; Tracking (education); Image sensor; Real-time computing; Object (grammar); Pattern recognition (psychology); Embedded system","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":[0.000178214,0.0002627624,0.0002407746,0.0002586091,0.0001838773,0.0003371284,0.000952395,0.0005840028,0.002936188],"category_scores_gemma":[0.0003374334,0.0001804795,0.0001873998,0.0002426212,0.0001511367,0.0006231495,0.0003512528,0.0004137079,0.0008697403],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004052922,"about_ca_system_score_gemma":0.0004021966,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003135818,"about_ca_topic_score_gemma":0.0006219104,"domain_scores_codex":[0.9997981,0.00001682281,0.000009497793,0.00004483996,0.0001092035,0.00002151008],"domain_scores_gemma":[0.9998186,0.00003138594,0.00002172435,0.00001718937,0.00009475998,0.00001640486],"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":[0.0002067275,0.0001253466,0.0009893849,0.000377746,0.00006082312,0.0002123087,0.00006119578,0.004341074,0.8012629,0.01122538,0.006721238,0.1744158],"study_design_scores_gemma":[0.0001373625,0.002000701,0.00420694,0.00006154712,0.0001515583,0.002589424,0.00004977531,0.1752431,0.7337412,0.004227372,0.07750092,0.00009017141],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06404523,0.001787722,0.9185683,0.0005419079,0.0005647104,0.0002028787,0.0003225744,0.003157009,0.0108097],"genre_scores_gemma":[0.5750371,0.0008002804,0.4109111,0.0006748108,0.0001612347,0.0001795484,0.0003773259,0.0000672539,0.01179124],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002936188,"threshold_uncertainty_score":0.009822488,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03205265640440047,"score_gpt":0.2537056962715113,"score_spread":0.2216530398671108,"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."}}