{"id":"W4388365601","doi":"10.1016/j.ibneur.2023.08.638","title":"A NOVEL DATA ANALYSIS PIPELINE FOR FIBER-BASED IN VIVO CALCIUM IMAGING","year":2023,"lang":"en","type":"article","venue":"IBRO Neuroscience Reports","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Amgen (Canada); University of Toronto; University of Waterloo; Toronto East General Hospital","funders":"","keywords":"Pipeline (software); Computer science; Calcium; In vivo; Data science; Medicine; Biology; Internal medicine","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001071552,0.0001643121,0.0002186246,0.0008297164,0.0002899065,0.0001710073,0.0005786952,0.00003633885,0.00002953665],"category_scores_gemma":[0.004286016,0.0001622879,0.0001077941,0.006325304,0.0002147022,0.0004548727,0.0001804431,0.0001301507,0.00001258071],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005269608,"about_ca_system_score_gemma":0.0001783365,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000754481,"about_ca_topic_score_gemma":0.00005663127,"domain_scores_codex":[0.9968196,0.00006547414,0.0005597967,0.001526524,0.0005539754,0.0004745882],"domain_scores_gemma":[0.9977586,0.0003331635,0.0003127004,0.001414986,0.00004703864,0.0001334775],"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":[0.00001064905,0.00008245807,0.001622443,0.00001287505,5.631751e-7,0.0001931638,0.00002796968,0.003794667,0.9914728,0.00003183483,0.000881116,0.001869391],"study_design_scores_gemma":[0.0001993151,0.00001393811,0.00612424,0.00000795849,0.00002666557,0.0001096583,0.00002022709,0.7677122,0.2081883,0.0001286088,0.01728948,0.0001793807],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5056774,0.00002121401,0.4678328,0.01382787,0.004614141,0.0025013,0.0004625477,0.002114562,0.002948153],"genre_scores_gemma":[0.9959041,0.000002676455,0.0006496211,0.001429177,0.00004858463,0.00008136663,0.00002342944,0.00002247715,0.001838569],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7832845,"threshold_uncertainty_score":0.6617911,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1468081029408809,"score_gpt":0.3614763768676191,"score_spread":0.2146682739267383,"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."}}