{"id":"W2403186868","doi":"10.2352/cic.2006.14.1.art00057","title":"Multispectral Colour Constancy","year":2006,"lang":"en","type":"article","venue":"Color and Imaging Conference","topic":"Color Science and Applications","field":"Physics and Astronomy","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Burnaby Hospital","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Multispectral image; RGB color model; Computer science; Artificial intelligence; Computer vision; Color constancy; Image (mathematics)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00004215855,0.00007653552,0.00008204439,0.0000233404,0.0001744097,0.0001097996,0.00009476501,0.000008068755,0.0002756285],"category_scores_gemma":[0.000001674584,0.00007221585,0.00002219257,0.00009087507,0.0001522474,0.0001195785,0.00003292292,0.00006091176,0.00003164115],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006601366,"about_ca_system_score_gemma":0.00005492007,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003502515,"about_ca_topic_score_gemma":0.00002235331,"domain_scores_codex":[0.9994758,0.000007808786,0.00009846115,0.0001840879,0.0000555701,0.0001783128],"domain_scores_gemma":[0.9997415,0.0000291074,0.00003527319,0.00009941903,0.0000502359,0.00004448546],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.000003382266,0.00006569105,0.2828206,0.000002496939,0.000003841108,0.000001396507,0.00009194477,0.000005647295,0.01650015,0.6759969,0.002487144,0.02202081],"study_design_scores_gemma":[0.001848012,0.00006721548,0.6641807,0.00005218396,0.0000577299,0.00001294379,0.001784285,0.06368914,0.01391667,0.1722632,0.08121715,0.0009107255],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8752173,0.00006565284,0.01813239,0.001201022,0.00006872065,0.0001710907,0.00002299648,0.00004831684,0.1050725],"genre_scores_gemma":[0.9983195,0.00000146643,0.0006550184,0.00005688164,0.00007404348,0.00003239198,0.000008291292,0.000003407052,0.0008489648],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5037336,"threshold_uncertainty_score":0.3017939,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004748952167981505,"score_gpt":0.2257697375758956,"score_spread":0.221020785407914,"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."}}