{"id":"W2050735977","doi":"10.4108/icst.immerscom2007.2114","title":"Fast Stroke Matching by Angle Quantization","year":2007,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Quantization (signal processing); Computer science; Similarity (geometry); Matching (statistics); Feature (linguistics); Feature matching; Dimension (graph theory); Artificial intelligence; Computer vision; Point (geometry); Space (punctuation); Task (project management); Pattern recognition (psychology); Feature extraction; Mathematics; Geometry; Image (mathematics); Combinatorics; Engineering","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":[0.0004077144,0.00006072352,0.00006851657,0.00006326867,0.00006280428,0.0001163924,0.0002192551,0.00003958683,0.00002643443],"category_scores_gemma":[0.000008024576,0.0000513573,0.00002417007,0.0002014578,0.000006433436,0.0003264172,0.00004310918,0.00004763158,0.000244054],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001570482,"about_ca_system_score_gemma":0.00001020415,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004186635,"about_ca_topic_score_gemma":0.00004397108,"domain_scores_codex":[0.9992927,0.00002339116,0.0001563326,0.0001689287,0.0001936386,0.0001650281],"domain_scores_gemma":[0.9996179,0.00005223167,0.00004470429,0.0001697268,0.000051652,0.00006381943],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00000849843,0.0002421815,0.008498199,0.00004823962,0.00004819214,0.00003447288,0.004257591,0.00008600135,0.1160115,0.1677343,0.03842634,0.6646045],"study_design_scores_gemma":[0.00347191,0.0004496842,0.02059836,0.0002198181,0.0000229513,0.0003633353,0.003771055,0.03537406,0.4949933,0.01015452,0.4281778,0.002403217],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01508522,0.00004033911,0.944392,0.0002308807,0.0003028594,0.00006858984,0.000001953508,0.0001970358,0.0396811],"genre_scores_gemma":[0.9721922,0.000002395872,0.02324213,0.0003402758,0.00007325399,0.000001759033,0.000005626057,0.000004832486,0.004137479],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.957107,"threshold_uncertainty_score":0.3136902,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01218096906038022,"score_gpt":0.248762383570032,"score_spread":0.2365814145096518,"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."}}