{"id":"W2061253069","doi":"10.1109/icassp.2014.6853787","title":"Adaptive windowing for optimal visualization of medical images based on normalized information distance","year":2014,"lang":"en","type":"article","venue":"","topic":"Computability, Logic, AI Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Visualization; Computer science; Entropy (arrow of time); Range (aeronautics); Computer vision; Parametric statistics; Artificial intelligence; Mutual information; High-dynamic-range imaging; Image processing; Similarity (geometry); Image (mathematics); High dynamic range; Dynamic range; Mathematics","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":[0.001225908,0.0001317581,0.0002113988,0.0001271769,0.00008154513,0.00009360941,0.0006791935,0.00008280079,0.00003141227],"category_scores_gemma":[0.0006237946,0.0001115106,0.000086037,0.0003057829,0.00006340081,0.0009662273,0.0001460466,0.00006692854,0.000007419264],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005029936,"about_ca_system_score_gemma":0.000100289,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002617289,"about_ca_topic_score_gemma":0.00000459183,"domain_scores_codex":[0.9982261,0.0001096797,0.0004223343,0.0002300062,0.0008024568,0.0002094192],"domain_scores_gemma":[0.9984739,0.0006202091,0.0001669441,0.0003674452,0.0002781608,0.00009334173],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002272876,0.0003139653,0.0007666884,0.0001835345,0.00002324631,9.661079e-7,0.001084001,0.08920651,0.00009938165,0.6307004,0.000982528,0.2764114],"study_design_scores_gemma":[0.001088026,0.0003772669,0.00129752,0.00003246477,0.000003529701,8.845173e-7,0.00002116535,0.991832,0.003039877,0.00123775,0.0009372203,0.0001323476],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001000612,0.000003662298,0.9957195,0.0006744885,0.0002229589,0.0003575302,0.000004900951,0.0001584164,0.001857933],"genre_scores_gemma":[0.7135612,6.838375e-7,0.2858313,0.0005038951,0.00004476913,0.00002761797,0.00001530419,0.000004552222,0.00001058331],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9026254,"threshold_uncertainty_score":0.4547272,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01239611620327696,"score_gpt":0.2723149394480491,"score_spread":0.2599188232447721,"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."}}