{"id":"W2042277556","doi":"10.1889/1.3069820","title":"59.2: Defining Dynamic Range","year":2008,"lang":"en","type":"article","venue":"SID Symposium Digest of Technical Papers","topic":"Color Science and Applications","field":"Physics and Astronomy","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dolby (Canada)","funders":"","keywords":"Luminance; Range (aeronautics); High dynamic range; Metric (unit); Dynamic range; Computer science; Computer graphics (images); Computer vision; Perception; Artificial intelligence; Mathematics; Engineering; Psychology; Operations management; Aerospace engineering","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":[0.00007476316,0.0001135059,0.0001746549,0.00004291887,0.0001754889,0.000009229414,0.0003012953,0.00004272136,0.0001519302],"category_scores_gemma":[0.000005963449,0.0001032489,0.0001366538,0.0002746542,0.0002434959,0.00008137496,0.00007497246,0.0001335918,0.00007016033],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002500693,"about_ca_system_score_gemma":0.00006209093,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006501444,"about_ca_topic_score_gemma":0.00001038161,"domain_scores_codex":[0.9991124,0.00001087269,0.0002277064,0.0002396817,0.0001811932,0.0002281973],"domain_scores_gemma":[0.9994081,0.00007456665,0.00008642636,0.000317353,0.00002993991,0.00008361632],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.000008706341,0.000191368,0.05080863,0.000005238457,0.00001326011,0.000001616849,0.00009660702,0.0001438836,0.9179247,0.03008407,0.0002714704,0.0004504525],"study_design_scores_gemma":[0.003119674,0.0008047001,0.8109509,0.0002054114,0.0002073095,0.00005788067,0.0009622994,0.0001946448,0.09452961,0.004286935,0.08247804,0.002202632],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3555246,0.00002993327,0.00002183982,0.0009199698,0.00004094933,0.000156066,0.00002117278,0.00007250937,0.643213],"genre_scores_gemma":[0.9991684,0.00001672568,0.0001544584,0.00005478633,0.00002854374,0.00006147446,0.00001504231,0.00001084835,0.0004897282],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8233951,"threshold_uncertainty_score":0.4210367,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007197597933067203,"score_gpt":0.2407360184229481,"score_spread":0.2335384204898809,"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."}}