{"id":"W2077756894","doi":"10.1117/1.3600770","title":"Detecting apoptosis using dynamic light scattering with optical coherence tomography","year":2011,"lang":"en","type":"letter","venue":"Journal of Biomedical Optics","topic":"Optical Coherence Tomography Applications","field":"Engineering","cited_by":61,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; Health Sciences Centre; Sunnybrook Health Science Centre; Toronto Metropolitan University; University of Toronto; Ontario Institute for Cancer Research","funders":"","keywords":"Optical coherence tomography; Intracellular; Frame rate; Optics; Light scattering; Dynamic light scattering; Coherence (philosophical gambling strategy); Speckle pattern; Apoptosis; Scattering; Materials science; Chemistry; Biophysics; Physics; Cell biology; Biology; Nanotechnology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000333758,0.000276713,0.0002208494,0.0004578101,0.0001915226,0.0004254478,0.0003335559,0.0007655458,0.0004824893],"category_scores_gemma":[0.0008968816,0.0001673905,0.00009720973,0.0003842512,0.0004033,0.0004291452,0.0003047989,0.0005311557,0.0002014347],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006561886,"about_ca_system_score_gemma":0.0002590569,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005156356,"about_ca_topic_score_gemma":0.001623135,"domain_scores_codex":[0.9996575,0.0000719709,0.00001608708,0.00004339649,0.0001690791,0.00004198026],"domain_scores_gemma":[0.9996827,0.0001211724,0.00005644652,0.00004091002,0.00006961287,0.00002913334],"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":[0.0001422599,0.00006189924,0.001362404,0.00005316451,0.000005066536,0.0004790655,0.0000501392,0.000475419,0.9761457,0.0007363209,0.0007915999,0.01969671],"study_design_scores_gemma":[0.00007757,0.0006678335,0.007418123,0.00002449623,0.00001722157,0.003281848,0.00008141275,0.04848621,0.9277657,0.00184481,0.01029599,0.00003878128],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8770627,0.00388522,0.1055148,0.003665781,0.0002230335,0.0001786456,0.0002375397,0.0004797519,0.008752477],"genre_scores_gemma":[0.8983167,0.002321345,0.09508944,0.0008045445,0.0001144256,0.0002253929,0.0001838482,0.00002270234,0.002921595],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0007655458,"threshold_uncertainty_score":0.004760981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01430958799613279,"score_gpt":0.2308456728068393,"score_spread":0.2165360848107065,"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."}}