{"id":"W2947685158","doi":"10.1002/sdtp.12985","title":"42‐1: <i>Invited Paper:</i> Bit‐Depth Constrained Black Level for High Dynamic Range Displays","year":2019,"lang":"en","type":"article","venue":"SID Symposium Digest of Technical Papers","topic":"Color Science and Applications","field":"Physics and Astronomy","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; BC Innovation Council","funders":"","keywords":"High dynamic range; Luminance; Dynamic range; Computer science; Computer vision; Range (aeronautics); Pixel; Tone mapping; Artificial intelligence; Computer graphics (images); Display device; Bit (key); SIGNAL (programming language); Shadow (psychology); Wide dynamic range; Depth perception; Perception; Materials science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001141155,0.0008290933,0.0003402464,0.0006020207,0.001383046,0.003018882,0.0009584593,0.002971297,0.02849285],"category_scores_gemma":[0.001199107,0.0003014533,0.0003686055,0.0004986956,0.0005369781,0.001397634,0.0007423129,0.001814077,0.01093151],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001183435,"about_ca_system_score_gemma":0.0007275781,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001461367,"about_ca_topic_score_gemma":0.003318496,"domain_scores_codex":[0.9993225,0.00008383929,0.00001585993,0.000147639,0.0003128593,0.0001173859],"domain_scores_gemma":[0.9990883,0.0001066705,0.00002886763,0.00003888952,0.0005824976,0.0001548298],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000672287,0.0001908492,0.001214188,0.0004512503,0.00003499399,0.0007411908,0.0003802791,0.0009312929,0.126414,0.009974664,0.7808684,0.07812655],"study_design_scores_gemma":[0.00006237983,0.0006564179,0.002567565,0.0001079067,0.00004393224,0.0006978162,0.0002766092,0.003677289,0.1074844,0.00168114,0.882638,0.0001066325],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1197286,0.03909782,0.0824905,0.07772622,0.1200468,0.001866643,0.003307702,0.004928925,0.5508066],"genre_scores_gemma":[0.2609999,0.01164662,0.02356857,0.009175725,0.01688557,0.000411526,0.001799984,0.001743482,0.6737686],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02849285,"threshold_uncertainty_score":0.09531802,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008910611803737636,"score_gpt":0.2471529376151805,"score_spread":0.2382423258114428,"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."}}