{"id":"W4306721492","doi":"10.32920/21290928","title":"Detecting cell death with optical coherence tomography and envelope statistics","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Optical Coherence Tomography Applications","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Sciences Centre; Toronto Metropolitan University; University of Toronto; University Health Network; Sunnybrook Health Science Centre; Ontario Institute for Cancer Research","funders":"Canadian Institutes of Health Research; Terry Fox Foundation; Canada Research Chairs; Cancer Care Ontario; American Institute of Ultrasound in Medicine","keywords":"Optical coherence tomography; Programmed cell death; In vivo; Envelope (radar); Mitosis; Coherence (philosophical gambling strategy); Apoptosis; Pathology; Medicine; Biomedical engineering; Cell biology; Biology; Statistics; Radiology; Computer science; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001362632,0.0003845306,0.0003282122,0.0002010554,0.0001470993,0.0001568218,0.0003511211,0.0001739551,0.0005835869],"category_scores_gemma":[0.00001467368,0.0003684493,0.00005489846,0.0003853095,0.0001291367,0.00005048622,0.0004539974,0.001238575,0.00001128516],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005241116,"about_ca_system_score_gemma":0.00006885015,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005074081,"about_ca_topic_score_gemma":0.00005423785,"domain_scores_codex":[0.9984151,0.0000255388,0.0003123513,0.0005293143,0.0003203162,0.00039741],"domain_scores_gemma":[0.9988422,0.0002684103,0.00005348769,0.0005388883,0.00007127717,0.0002257502],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002322047,0.001245487,0.2413578,0.01125758,0.002553176,0.0004876358,0.003373787,0.3185717,0.008148274,0.2532874,0.005258273,0.1542267],"study_design_scores_gemma":[0.005369893,0.003074008,0.3258074,0.001058361,0.003231921,0.0002783487,0.004971583,0.4722541,0.03896293,0.1025821,0.02355773,0.01885165],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2668484,0.0006197902,0.6036381,0.0000432306,0.0002212061,0.001524025,0.0005434903,0.001626913,0.1249349],"genre_scores_gemma":[0.6924169,0.00007022287,0.3068025,0.00001778468,0.00002307492,0.0004725147,0.00006192562,0.00005944272,0.00007563601],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4255686,"threshold_uncertainty_score":0.9998767,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01443495138207964,"score_gpt":0.2290613713641974,"score_spread":0.2146264199821177,"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."}}