{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002337129,0.0001793612,0.0005257746,0.00081645,0.00008262919,0.00005233421,0.0002456664,0.0001603287,0.001073261],"category_scores_gemma":[0.002510468,0.0001779217,0.0001299668,0.0004795976,0.0002183721,0.00004528492,0.000694899,0.001241516,0.000002864944],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004653667,"about_ca_system_score_gemma":0.0004538883,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003604006,"about_ca_topic_score_gemma":0.0000148758,"domain_scores_codex":[0.997148,0.0002997826,0.0004475129,0.0005547021,0.000983194,0.0005668396],"domain_scores_gemma":[0.998035,0.0007070432,0.00006565635,0.0005533898,0.000490125,0.0001488129],"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":[0.003013139,0.005285486,0.1873338,0.03687696,0.0002389047,0.0007681475,0.003758558,0.0001544585,0.4226694,0.2089971,0.09436927,0.03653469],"study_design_scores_gemma":[0.006977248,0.007596517,0.02551705,0.01191102,0.0002264161,0.00007874228,0.003830917,0.1848827,0.544669,0.1062368,0.1062149,0.00185884],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4859568,0.005354192,0.1941537,0.1064871,0.001174014,0.03741541,0.001747241,0.002305547,0.165406],"genre_scores_gemma":[0.9813178,0.0001873279,0.01491759,0.0001505671,0.0001882049,0.0009954541,0.0003144608,0.00009038976,0.001838161],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.495361,"threshold_uncertainty_score":0.9998399,"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."}}