{"id":"W4394871231","doi":"10.1117/12.3005517","title":"MLDIPS: enabling improved PS-OCT contrast through maximum likelihood estimation","year":2024,"lang":"en","type":"article","venue":"","topic":"Optical Coherence Tomography Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Contrast (vision); Maximum likelihood; Computer science; Estimation; Artificial intelligence; Statistics; Mathematics; 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":[0.0000944077,0.000162126,0.0001277034,0.00008496986,0.00005560759,0.0002005542,0.0001341425,0.00009291653,0.0002883324],"category_scores_gemma":[0.00002699539,0.0001498646,0.00008145312,0.0005435324,0.00003502561,0.0003873087,0.00001921185,0.0002176799,0.000359739],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004721299,"about_ca_system_score_gemma":0.00002090039,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003037705,"about_ca_topic_score_gemma":0.0000157379,"domain_scores_codex":[0.9990975,0.000007129618,0.0002364557,0.0002345558,0.000115532,0.0003087824],"domain_scores_gemma":[0.999508,0.0001065668,0.000009345759,0.0002500058,0.00004818567,0.00007792599],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000008229845,0.0001379342,0.00006919031,0.0008858811,0.0004327441,0.00001719115,0.0009924984,0.01835012,0.1464573,0.1296565,0.009199087,0.6937933],"study_design_scores_gemma":[0.0001471298,0.00002847664,0.00009976018,0.00005685154,0.00004720545,0.000006024844,0.0001042212,0.9458929,0.01050735,0.03485708,0.007983789,0.0002691546],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008799149,0.001251917,0.9325126,0.0005081033,0.0003413806,0.0004373979,0.00002554673,0.003018731,0.05310514],"genre_scores_gemma":[0.9606807,0.00006817092,0.03876467,0.00007801564,0.0001007936,0.0001633111,0.00002296326,0.00004470592,0.00007667399],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9518815,"threshold_uncertainty_score":0.61113,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01078089569167857,"score_gpt":0.24621366878454,"score_spread":0.2354327730928614,"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."}}