{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004757178,0.0003061056,0.0001828547,0.001111089,0.000107951,0.0005541019,0.0002246349,0.0002956838,0.0006480962],"category_scores_gemma":[0.002127571,0.0001572802,0.0001514068,0.0007676065,0.0005572421,0.0006624076,0.0003498889,0.0002922829,0.0002131227],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004923288,"about_ca_system_score_gemma":0.0003042264,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007769249,"about_ca_topic_score_gemma":0.001083528,"domain_scores_codex":[0.9995459,0.00009502161,0.00001956824,0.00005337752,0.0002526836,0.00003337819],"domain_scores_gemma":[0.9988582,0.0005296337,0.0003032225,0.0001197063,0.0001625972,0.00002647377],"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":[0.0004774644,0.0001453307,0.01561449,0.0001758403,0.00007224432,0.0002173833,0.0001929166,0.05184904,0.7130672,0.01016405,0.000999178,0.2070249],"study_design_scores_gemma":[0.00003290044,0.000236997,0.02610973,0.00003039997,0.00003088989,0.0008080435,0.0001037576,0.5584915,0.4035027,0.007894983,0.002687652,0.00007044859],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4566742,0.0005271226,0.538086,0.0002027648,0.00002090481,0.00004730656,0.0003013334,0.001059173,0.0030813],"genre_scores_gemma":[0.8624825,0.0003865219,0.1350629,0.00009032636,0.00001891716,0.0000464134,0.0002594864,0.0001014978,0.001551619],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001111089,"threshold_uncertainty_score":0.003572166,"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."}}