{"id":"W2074031628","doi":"10.1117/12.709738","title":"Perception of dim targets on dark backgrounds in MRI","year":2007,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Artificial intelligence; Computer science; Computer vision; Thresholding; Pixel; Phase congruency; Observer (physics); Wavelet; Pattern recognition (psychology); Noise (video); Contrast (vision); Artifact (error); Feature extraction; Physics; Image (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":[0.002180265,0.0002707079,0.0004088253,0.0002300919,0.00006344821,0.0001029339,0.001447059,0.0001842008,0.00000645051],"category_scores_gemma":[0.0004590272,0.0002303154,0.0004787716,0.0005916902,0.0001847555,0.0007929825,0.0002152806,0.0003499775,0.000001814719],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00019463,"about_ca_system_score_gemma":0.00003179112,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000161228,"about_ca_topic_score_gemma":1.830038e-7,"domain_scores_codex":[0.997497,3.420286e-8,0.000800694,0.0004284391,0.0008395044,0.0004343756],"domain_scores_gemma":[0.9980292,0.000270173,0.000361451,0.00008900778,0.001148085,0.0001020326],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001179109,0.0001675367,0.0004245059,0.0002176953,0.00008757449,2.937619e-7,0.0005425164,0.0001184977,0.5889964,0.4060159,0.001017769,0.002293451],"study_design_scores_gemma":[0.006310625,0.002506871,0.04024809,0.001762062,0.0001687265,0.00006556362,0.00496007,0.1355667,0.7747763,0.02771602,0.004339531,0.001579409],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9845533,0.00005800712,0.008690461,0.001144884,0.0003082081,0.0003227092,0.000007265854,0.00005884929,0.004856285],"genre_scores_gemma":[0.58547,0.00004220169,0.4139498,0.0001394432,0.0002119428,0.00002386051,0.000002563071,0.00002980784,0.0001303042],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4052594,"threshold_uncertainty_score":0.9391988,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0140858565216292,"score_gpt":0.2594058408653438,"score_spread":0.2453199843437146,"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."}}