{"id":"W2132959774","doi":"10.1109/robio.2005.246287","title":"Adaptive color claddification with gaussian mixture model","year":2005,"lang":"en","type":"article","venue":"","topic":"Color Science and Applications","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Artificial intelligence; Mixture model; Color space; Computer science; Computer vision; Color model; Pattern recognition (psychology); Representation (politics); Color balance; Gaussian; Adaptation (eye); Color quantization; Color histogram; Color normalization; Color image; Mathematics; Image (mathematics); Image processing; Optics","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":[0.0009093396,0.000754681,0.0009806443,0.001125948,0.0004757706,0.0008695832,0.001683322,0.00105255,0.001674441],"category_scores_gemma":[0.00236139,0.0005001876,0.00114955,0.0009744189,0.0006471102,0.001138209,0.0009124351,0.001348193,0.001341012],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009632004,"about_ca_system_score_gemma":0.0006232674,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01023853,"about_ca_topic_score_gemma":0.006920534,"domain_scores_codex":[0.9993045,0.0001210848,0.00002598323,0.0002223879,0.0002500877,0.00007598915],"domain_scores_gemma":[0.9994119,0.0002030654,0.00004761871,0.0001053942,0.0002051219,0.00002683014],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002508936,0.00007368499,0.001597441,0.00006870208,0.0001197273,0.00009082636,0.0001188022,0.355616,0.0240559,0.01448568,0.003790144,0.5997323],"study_design_scores_gemma":[0.000004609905,0.000008691914,0.0002109623,0.000002236203,0.000008395369,0.00002840756,0.000004135103,0.9937139,0.002637362,0.002363758,0.001007055,0.00001044497],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003744087,0.0001219727,0.994785,0.00004883768,0.00003455246,0.00001267618,0.0000153232,0.000870542,0.0003670129],"genre_scores_gemma":[0.2505263,0.0003967926,0.742228,0.0001988219,0.0001053937,0.0001016839,0.0002520903,0.0003868898,0.005804063],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01023853,"threshold_uncertainty_score":0.02035785,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01236877536372811,"score_gpt":0.2424479513313102,"score_spread":0.2300791759675821,"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."}}