{"id":"W4309150252","doi":"10.21203/rs.3.rs-2198041/v1","title":"Shadow imaging for panoptical visualization of brain tissue in vivo","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Optical Imaging and Spectroscopy Techniques","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Research Foundation of Korea; Agence Nationale de la Recherche; Alberta Innovates; National Research Foundation","keywords":"Shadow (psychology); Visualization; Neuroimaging; Brain tissue; In vivo; Preclinical imaging; Computer science; Neuroscience; Computer vision; Artificial intelligence; Psychology; Biology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001706562,0.0005247716,0.0002452072,0.0004955555,0.000229619,0.0004943478,0.0002088078,0.0003598605,0.006075486],"category_scores_gemma":[0.0004665738,0.0002399361,0.0001520945,0.0003321268,0.0003987075,0.0007794619,0.0005877259,0.0004952344,0.000741171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002429763,"about_ca_system_score_gemma":0.0003463991,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005328989,"about_ca_topic_score_gemma":0.0008844369,"domain_scores_codex":[0.9999349,0.00001446959,0.000002505743,0.00001143326,0.00002758683,0.000009183546],"domain_scores_gemma":[0.9998072,0.00009970323,0.00001625944,0.00003340022,0.0000268599,0.00001674035],"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":[0.0004089731,0.00004665556,0.0004204117,0.0002851593,0.00002243921,0.000259665,0.0001712205,0.004578077,0.8670477,0.03331394,0.002232861,0.09121286],"study_design_scores_gemma":[0.00006202643,0.0001865522,0.003693097,0.00005338243,0.00005055681,0.002124031,0.0001624151,0.2133335,0.7276559,0.02984406,0.02279897,0.00003548212],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1234537,0.003477436,0.8488072,0.0004000213,0.0001771421,0.00008401222,0.000306459,0.0009931177,0.02230099],"genre_scores_gemma":[0.5643408,0.005373627,0.4081841,0.0001788231,0.0001678786,0.0001256494,0.0003343801,0.0004936184,0.02080112],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006075486,"threshold_uncertainty_score":0.02032447,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05934476565561234,"score_gpt":0.5112797079224175,"score_spread":0.4519349422668051,"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."}}