{"id":"W1999129255","doi":"10.1080/10867651.2002.10487554","title":"Parameter Estimation for Photographic Tone Reproduction","year":2002,"lang":"en","type":"article","venue":"Journal of Graphics Tools","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":130,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Utah; Ryerson University; U.S. Department of Energy","keywords":"Computer science; Tone (literature); Set (abstract data type); Photography; Range (aeronautics); Operator (biology); Process (computing); Tone mapping; Pixel; Sample (material); Code (set theory); Computer vision; Artificial intelligence; Algorithm; High dynamic range; Dynamic range; Engineering; Art","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009498594,0.0007198395,0.000518033,0.0007723601,0.0003575803,0.001020912,0.0007252528,0.0009956638,0.003515959],"category_scores_gemma":[0.009736936,0.0003686866,0.000394046,0.0004589321,0.0003687954,0.0006667515,0.0007653711,0.001041666,0.001404473],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003324448,"about_ca_system_score_gemma":0.0004629618,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002137284,"about_ca_topic_score_gemma":0.001622379,"domain_scores_codex":[0.9992183,0.0002867132,0.00004298288,0.0001560473,0.0002515872,0.00004436351],"domain_scores_gemma":[0.9976469,0.001427047,0.0001908344,0.0003286028,0.0003641938,0.00004242769],"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":[0.0003365454,0.00008318619,0.002417954,0.0002143505,0.0000615371,0.0001664507,0.0002757362,0.1476959,0.07501633,0.007438997,0.003758618,0.7625344],"study_design_scores_gemma":[0.00002873192,0.00007029015,0.001959317,0.00002332067,0.00002279785,0.0002499097,0.00004179191,0.946741,0.04359509,0.003417043,0.003810989,0.00003967797],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008215113,0.0000865257,0.9901215,0.0000339321,0.00001010063,0.00002331574,0.00003198134,0.001028583,0.0004489357],"genre_scores_gemma":[0.322252,0.000163773,0.6748665,0.00005417049,0.0000272299,0.0001126341,0.0002539444,0.0003639525,0.001905731],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003515959,"threshold_uncertainty_score":0.01176208,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05001962707591785,"score_gpt":0.3034879551484206,"score_spread":0.2534683280725028,"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."}}