{"id":"W1953078193","doi":"10.1109/mce.2015.2463294","title":"Demystifying High-Dynamic-Range Technology: A new evolution in digital media","year":2015,"lang":"en","type":"article","venue":"IEEE Consumer Electronics Magazine","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":57,"is_retracted":false,"has_abstract":true,"ca_institutions":"Telus (Canada); University of British Columbia","funders":"","keywords":"Luminance; High dynamic range; Dynamic range; Human eye; High-dynamic-range imaging; Computer science; Computer graphics (images); Optics; Range (aeronautics); Adaptation (eye); Digital photography; Tone mapping; Computer vision; Artificial intelligence; Photography; Physics; Engineering; Art","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003718404,0.0002736345,0.0002999545,0.0006668964,0.00004686011,0.000180049,0.001098542,0.0001881456,0.000005510674],"category_scores_gemma":[0.000270086,0.0002914614,0.00004508357,0.001519186,0.0001006433,0.001229271,0.0002373462,0.0005104691,0.0003705627],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008856228,"about_ca_system_score_gemma":0.0007828067,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000333999,"about_ca_topic_score_gemma":0.0003972613,"domain_scores_codex":[0.9977552,0.00003137904,0.000388742,0.0005903333,0.000411077,0.0008233186],"domain_scores_gemma":[0.9987136,0.00006925761,0.0001361077,0.0007288141,0.0001876379,0.0001645689],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001720582,0.0008142758,0.01732816,0.00006787873,0.0001862494,0.0004412885,0.001217892,0.0001758904,0.09286699,0.1811363,0.03982377,0.6657692],"study_design_scores_gemma":[0.008866659,0.001207839,0.006528931,0.000382936,0.00008832291,0.000449329,0.0000513721,0.07949387,0.05662392,0.7987425,0.04448202,0.003082258],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07871441,0.006276855,0.9097084,0.002437773,0.0004907027,0.000416579,0.000005325675,0.001308312,0.000641587],"genre_scores_gemma":[0.9630948,0.0001277984,0.03602758,0.0000915147,0.00004366057,0.00004579739,0.00001199949,0.0000302125,0.0005266086],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8843804,"threshold_uncertainty_score":0.9999537,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01393683043392471,"score_gpt":0.2505120857818698,"score_spread":0.2365752553479451,"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."}}