{"id":"W4293085771","doi":"10.32920/19400735","title":"Dynamic light scattering optical coherence tomography to probe motion of subcellular scatterers","year":2022,"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; St. Michael's Hospital","funders":"Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada","keywords":"Decorrelation; Optical coherence tomography; Scattering; Dynamic light scattering; Physics; Coherence (philosophical gambling strategy); Chemistry; Biophysics; Optics; Biology; Mathematics; Algorithm; Quantum mechanics","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002480183,0.0004880567,0.000551888,0.0007100251,0.00008275658,0.00008605886,0.001006948,0.0002746914,0.001156973],"category_scores_gemma":[0.00001616908,0.0005530454,0.0003584883,0.001113648,0.0001176453,0.00008861098,0.0008501831,0.0009435054,0.00007241727],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001753219,"about_ca_system_score_gemma":0.00003887563,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003951503,"about_ca_topic_score_gemma":0.00002898651,"domain_scores_codex":[0.997445,0.00004058117,0.0007234173,0.0007462555,0.0005226598,0.0005220176],"domain_scores_gemma":[0.9981932,0.0000614665,0.00009457542,0.001252588,0.0001111862,0.0002869692],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005864119,0.0008654756,0.01473702,0.005309877,0.001014807,0.00002832618,0.001335371,0.4409362,0.4974993,0.01498536,0.001772037,0.02145758],"study_design_scores_gemma":[0.002462211,0.001516418,0.1760399,0.003029548,0.001933094,0.00007104925,0.00223506,0.3809745,0.3682497,0.03864444,0.01090264,0.01394152],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.60848,0.0004164257,0.3585491,0.001049854,0.000831392,0.003362115,0.0001852314,0.001616961,0.02550901],"genre_scores_gemma":[0.9555842,0.00002251389,0.04258768,0.00004558966,0.00002608148,0.001427728,0.0001244399,0.00009763495,0.00008413869],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3471042,"threshold_uncertainty_score":0.9997561,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01038937072007532,"score_gpt":0.2333677303389686,"score_spread":0.2229783596188933,"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."}}