{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001188682,0.0001558156,0.000189511,0.0002030956,0.0006106091,0.00008139663,0.0003187542,0.0001818235,0.0008635088],"category_scores_gemma":[0.0001703424,0.0001606372,0.00007220414,0.001007887,0.0001839199,0.0001701792,0.00004311299,0.0002238568,0.001183131],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002073561,"about_ca_system_score_gemma":0.00005182465,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001799845,"about_ca_topic_score_gemma":0.000289034,"domain_scores_codex":[0.9978297,0.0002267687,0.0003438134,0.0005980055,0.0003804483,0.0006212005],"domain_scores_gemma":[0.998058,0.0004121565,0.0001184197,0.0005170102,0.0007575925,0.0001368868],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000535268,0.0006073137,0.0006092545,0.00006226984,0.0000668538,0.000001351686,0.01722462,0.0269083,0.008254938,0.7814149,0.1468934,0.01742156],"study_design_scores_gemma":[0.0007832124,0.0003530805,0.002600307,0.000005803697,0.00002052431,0.000002740647,0.001082942,0.9587285,0.0000580465,0.01229107,0.0238716,0.0002021764],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.159832,0.0005515649,0.8191259,0.001232843,0.0002390084,0.004564278,0.00006923299,0.0002474442,0.01413774],"genre_scores_gemma":[0.9728161,0.0001074882,0.002769032,0.000152735,0.0005041015,0.00590417,0.000504635,0.00005615392,0.01718563],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9318202,"threshold_uncertainty_score":0.9995946,"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."}}