{"id":"W2003118276","doi":"10.4018/jssci.2009062501","title":"On Visual Semantic Algebra (VSA)","year":2009,"lang":"en","type":"article","venue":"International Journal of Software Science and Computational Intelligence","topic":"Cognitive Computing and Networks","field":"Computer Science","cited_by":63,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Visual Objects; Artificial intelligence; Cognitive architecture; Object (grammar); Process (computing); Set (abstract data type); Cognition; Inference; Process calculus; Cognitive neuroscience of visual object recognition; Perception; Visual perception; Cognitive science; Pattern recognition (psychology); Theoretical computer science; Programming language","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":[0.00103708,0.000146409,0.0001621935,0.0005569377,0.0002089223,0.0004994581,0.00161158,0.00003574795,0.00001137327],"category_scores_gemma":[0.0008519061,0.0001266189,0.00007578398,0.0006745142,0.0002758563,0.0009159883,0.0001829665,0.0002557751,0.00002394848],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001045329,"about_ca_system_score_gemma":0.0004187409,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002071778,"about_ca_topic_score_gemma":3.245735e-7,"domain_scores_codex":[0.9971887,0.00003815982,0.0004573723,0.0003312558,0.001746358,0.0002381194],"domain_scores_gemma":[0.9958885,0.0006109024,0.0003128315,0.0001089725,0.002900413,0.0001784207],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00003385619,0.0001515649,0.0002003172,0.000001448285,0.00002061621,0.00009215067,0.000295323,0.02462284,0.00006083492,0.1063124,0.0002860348,0.8679227],"study_design_scores_gemma":[0.0003206972,0.001281746,0.02457405,0.0003390945,0.000008944309,0.001102794,0.00006619975,0.4375109,0.001542945,0.5326089,0.0002973414,0.0003464789],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07598288,0.0001470677,0.9200256,0.002400301,0.001069935,0.00004812517,9.734375e-7,0.0000446309,0.0002804647],"genre_scores_gemma":[0.9648489,0.0000400097,0.03181678,0.003066119,0.000206095,4.696061e-7,7.882646e-7,0.000003424083,0.00001738207],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8888661,"threshold_uncertainty_score":0.516337,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01617764347067332,"score_gpt":0.3143102894051346,"score_spread":0.2981326459344613,"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."}}