{"id":"W1987369582","doi":"10.1364/josaa.28.002385","title":"Spectral color constancy using a maximum entropy approach","year":2011,"lang":"en","type":"article","venue":"Journal of the Optical Society of America A","topic":"Color Science and Applications","field":"Physics and Astronomy","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Maximum entropy spectral estimation; A priori and a posteriori; Principle of maximum entropy; Entropy (arrow of time); Computer science; Algorithm; Color constancy; Basis (linear algebra); Mathematics; Pattern recognition (psychology); Artificial intelligence; Physics; Image (mathematics)","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":[0.0001166503,0.00006830604,0.000182284,0.000008732834,0.0001015178,0.0000133498,0.0003109768,0.00001860143,0.0001727698],"category_scores_gemma":[0.000004638812,0.00004292518,0.000425402,0.0002277166,0.0004228465,0.0000783611,0.00005830314,0.0001788903,0.000002449206],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002555845,"about_ca_system_score_gemma":0.0001119532,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003674276,"about_ca_topic_score_gemma":3.144003e-8,"domain_scores_codex":[0.9993044,0.00001659388,0.0002535748,0.00007484001,0.000190648,0.0001599777],"domain_scores_gemma":[0.9993586,0.00003178279,0.0003231331,0.0001293321,0.00008197203,0.00007517091],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0005343778,0.01194889,0.1383876,0.0001172889,0.002859632,0.000004926079,0.03737378,0.004710417,0.4422553,0.257322,0.0297541,0.07473166],"study_design_scores_gemma":[0.009193369,0.003339992,0.09678697,0.0004772629,0.002654217,0.0003061572,0.1301528,0.2313073,0.1549742,0.3307741,0.03755763,0.002476057],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8358777,0.00003095482,0.1492036,0.0008931911,0.00009361946,0.000151927,0.000006911787,0.000004030088,0.0137381],"genre_scores_gemma":[0.831567,0.000003373447,0.1682002,0.0000920262,0.00009894236,0.000001321999,1.298579e-7,0.00000370031,0.00003333905],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2872812,"threshold_uncertainty_score":0.1891708,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02622714523004201,"score_gpt":0.2541120559434782,"score_spread":0.2278849107134362,"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."}}