{"id":"W4394006576","doi":"10.1117/12.3016664","title":"Visualizing densely interwoven fiber networks in biological tissues using Computational Scattered Light Imaging","year":2024,"lang":"en","type":"article","venue":"","topic":"Optical Imaging and Spectroscopy Techniques","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Visualization; Fiber; Light scattering; Optics; Materials science; Physics; Artificial intelligence; Scattering","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":[0.000306653,0.0005307068,0.0002664309,0.0008353198,0.0003556701,0.001113972,0.0004832218,0.0006905503,0.001148162],"category_scores_gemma":[0.0009343864,0.000381687,0.0003092849,0.0006454361,0.0006229904,0.0009017044,0.0006541256,0.0005779158,0.0001323123],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004733645,"about_ca_system_score_gemma":0.0007538602,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003276511,"about_ca_topic_score_gemma":0.004361221,"domain_scores_codex":[0.9999099,0.00002438095,0.000003801719,0.00001807656,0.00003013337,0.0000137346],"domain_scores_gemma":[0.9995468,0.000278909,0.00006432163,0.00002881597,0.00004645629,0.00003462785],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001676438,0.0001134524,0.004387549,0.0001794983,0.00005747571,0.0005082755,0.0005053784,0.8754945,0.07822894,0.01476011,0.0006941594,0.02490297],"study_design_scores_gemma":[0.000005596939,0.00001076123,0.00064881,0.000007965625,0.000005891306,0.00009031019,0.00007345332,0.9900699,0.00477459,0.003947069,0.0003559718,0.000009597507],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.465057,0.0003602216,0.5300628,0.0004466548,0.00002076389,0.00005334294,0.0001987705,0.0005418899,0.003258558],"genre_scores_gemma":[0.8525375,0.0003856812,0.1451295,0.00005763243,0.00001727007,0.00004250709,0.0001573974,0.000104079,0.001568467],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003276511,"threshold_uncertainty_score":0.006514847,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03247823869391193,"score_gpt":0.381661552807852,"score_spread":0.3491833141139401,"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."}}