{"id":"W4211132728","doi":"10.32920/ryerson.14655897","title":"Adaptive Exposure Fusion for HDR Imaging","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Sequence (biology); Computer science; Fusion; Artificial intelligence; Metric (unit); Image fusion; Computer vision; Multiple exposure; Image (mathematics); Pattern recognition (psychology); 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":[0.0003151029,0.0002485981,0.0002569202,0.0001159194,0.00008711797,0.0004641446,0.001246902,0.0001168347,0.00006288318],"category_scores_gemma":[0.00003448478,0.0002397947,0.0001722761,0.0001162988,0.00002386439,0.0003957636,0.003571418,0.0002938004,0.000007726595],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009763891,"about_ca_system_score_gemma":0.0001829646,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003827564,"about_ca_topic_score_gemma":0.00001193056,"domain_scores_codex":[0.9982876,0.00005024342,0.0002631613,0.0008340003,0.0002744944,0.0002904601],"domain_scores_gemma":[0.9983346,0.00006492838,0.0001446493,0.001081975,0.0003246653,0.00004914554],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002545083,0.0003285928,0.000230334,0.0005190942,0.0001323297,0.0001579931,0.003073035,0.0001029621,0.0337782,0.06471759,0.07157785,0.8253565],"study_design_scores_gemma":[0.0005212291,0.0001757479,0.0002470803,0.0007315503,0.00003064075,0.00001890605,0.0002767696,0.3394774,0.6081505,0.03840658,0.01059464,0.001368941],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0004338258,0.0004453305,0.9860039,0.0009440454,0.0008745802,0.0007248527,0.000004417374,0.0009739151,0.009595163],"genre_scores_gemma":[0.1571518,0.00003661758,0.8389052,0.0006750521,0.0001299682,0.0003306259,0.00002439577,0.00001915793,0.002727245],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8239876,"threshold_uncertainty_score":0.9778543,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02304153353440056,"score_gpt":0.2757639509186194,"score_spread":0.2527224173842188,"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."}}