{"id":"W1980605853","doi":"10.1002/col.10060","title":"A quantitative network model for color categorization","year":2002,"lang":"en","type":"article","venue":"Color Research & Application","topic":"Categorization, perception, and language","field":"Psychology","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Categorical variable; Chromaticity; Color space; Categorization; Artificial intelligence; Color model; Pattern recognition (psychology); Computer science; Color balance; Color vision; Colored; Computer vision; Color image; Image (mathematics); Image processing; Machine learning","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":[0.001889093,0.0007312413,0.0008099499,0.001627362,0.000650025,0.001586803,0.002032581,0.001611636,0.00776147],"category_scores_gemma":[0.005854688,0.000453013,0.0009389611,0.000892133,0.001554375,0.003531324,0.0008884494,0.001126123,0.0008243902],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002844515,"about_ca_system_score_gemma":0.0006835939,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007144591,"about_ca_topic_score_gemma":0.00460649,"domain_scores_codex":[0.9993199,0.0002722436,0.00002187356,0.0001824828,0.0001252995,0.00007808522],"domain_scores_gemma":[0.99814,0.001064979,0.0002211439,0.0001159409,0.0003319339,0.0001259574],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006626343,0.00004081838,0.0008968118,0.00005873772,0.00004994137,0.00008520775,0.0001388861,0.6526095,0.001372222,0.3326883,0.001534712,0.01045874],"study_design_scores_gemma":[0.000006566105,0.000004795882,0.00008779271,0.000002786459,0.000003158728,0.00001285784,0.000005487583,0.9329218,0.00002921417,0.06666143,0.0002604924,0.000003667929],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06740382,0.0004114866,0.9149517,0.001506641,0.00006481033,0.00008415611,0.0005192707,0.0004000466,0.01465801],"genre_scores_gemma":[0.9103498,0.0003918557,0.07390337,0.0001889162,0.0001027115,0.0004295226,0.0004838219,0.0001217112,0.01402834],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00776147,"threshold_uncertainty_score":0.02596474,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2350077010675141,"score_gpt":0.4681063710716956,"score_spread":0.2330986700041815,"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."}}