{"id":"W2059657882","doi":"10.1142/s0219467806002379","title":"REINFORCED CONTRAST ADAPTATION","year":2006,"lang":"en","type":"article","venue":"International Journal of Image and Graphics","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Contrast (vision); Computer science; Artificial intelligence; Observer (physics); Image (mathematics); Reinforcement learning; Histogram; Computer vision; Ideal (ethics); Adaptation (eye); Transformation (genetics); Point (geometry); Algorithm; 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":[0.0001539447,0.00005215594,0.00007101935,0.0001869148,0.00003489088,0.0001645246,0.0002981081,0.00001565393,0.000004401364],"category_scores_gemma":[0.0000472609,0.00004354348,0.00005048731,0.00008943408,0.00003811101,0.001100754,0.00004118486,0.00009934278,0.000001739365],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001000905,"about_ca_system_score_gemma":0.00002577462,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001250561,"about_ca_topic_score_gemma":0.000002059651,"domain_scores_codex":[0.9993174,0.00001312782,0.0002486811,0.00006561716,0.0002877992,0.00006736072],"domain_scores_gemma":[0.9991354,0.00005130254,0.0002044247,0.00005653388,0.0005171729,0.00003518465],"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":[0.00008235862,0.000075583,0.001433618,0.000007137588,0.00009313427,0.0004532512,0.0006168139,0.0008322199,0.03683848,0.6744527,0.004499292,0.2806154],"study_design_scores_gemma":[0.004520856,0.0002998972,0.02841844,0.0002261614,0.00002434116,0.002184508,0.0002897047,0.7553355,0.01668862,0.150802,0.04073518,0.0004747419],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007890158,0.0002005835,0.9885034,0.002101054,0.0003816309,0.00001727272,0.000001010309,0.00001117863,0.0008937298],"genre_scores_gemma":[0.8984616,0.000191808,0.1003079,0.0007297092,0.0001900867,2.902987e-7,0.000001398897,0.000003350118,0.0001138524],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8905714,"threshold_uncertainty_score":0.1775652,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00847445703570523,"score_gpt":0.2639550271641977,"score_spread":0.2554805701284925,"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."}}