{"id":"W4230495591","doi":"10.32920/ryerson.14648115","title":"Decorrelation Time Analysis Using Dynamic Light Scattering with Optical Coherence Tomography in an in vivo Mouse Tumour Model","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Optical Coherence Tomography Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; University of Toronto","funders":"","keywords":"Optical coherence tomography; Decorrelation; Speckle pattern; In vivo; Repeatability; Interferometry; Optics; Coherence (philosophical gambling strategy); Biomedical engineering; Optical tomography; Pixel; Preclinical imaging; Physics; Nuclear medicine; Computer science; Chemistry; Medicine; Biology; Computer vision","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":[0.0006047168,0.0007060183,0.0004079091,0.001165241,0.0001533925,0.0003637107,0.0003250681,0.0004882009,0.0005986896],"category_scores_gemma":[0.0003134033,0.0002584373,0.0004113445,0.0007102059,0.0002911035,0.0002944037,0.0002282652,0.0005575178,0.0001822001],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003643938,"about_ca_system_score_gemma":0.0002657041,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001750867,"about_ca_topic_score_gemma":0.001964766,"domain_scores_codex":[0.9997666,0.00002985137,0.00001361214,0.00006046255,0.0001036131,0.00002586658],"domain_scores_gemma":[0.9996124,0.0000934661,0.0001305103,0.00005120924,0.00007777048,0.00003463236],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.000100756,0.00004127482,0.0001779884,0.00002225469,0.000006663253,0.00002502081,0.00001507275,0.001767065,0.9960432,0.0001118214,0.00002789386,0.00166091],"study_design_scores_gemma":[0.00001298144,0.000388422,0.002799539,0.000005706598,0.00003282702,0.00008881166,0.00001350385,0.03271544,0.9628683,0.0001322977,0.0009254445,0.0000166866],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8276916,0.0009380301,0.1678166,0.000103085,0.00004005073,0.0001100263,0.001030719,0.0006846498,0.001585252],"genre_scores_gemma":[0.8760083,0.001270107,0.1172945,0.00007580264,0.00001708512,0.0002678456,0.0009448675,0.0002466855,0.003874841],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001750867,"threshold_uncertainty_score":0.003481388,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01126777598618908,"score_gpt":0.2430563299858294,"score_spread":0.2317885539996403,"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."}}