{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00299799,0.002484492,0.001232659,0.003392723,0.00113417,0.003816315,0.00294237,0.00138482,0.01925997],"category_scores_gemma":[0.007819122,0.001098634,0.001702836,0.002562963,0.0007141819,0.003101059,0.003303137,0.002697573,0.01761645],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001065177,"about_ca_system_score_gemma":0.003513316,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004559036,"about_ca_topic_score_gemma":0.00604829,"domain_scores_codex":[0.9980356,0.0001401031,0.0002731413,0.0005403743,0.0008528207,0.000157989],"domain_scores_gemma":[0.995518,0.0009837364,0.0003272006,0.001140933,0.001735572,0.0002945655],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001191163,0.0004565103,0.004332635,0.0007611939,0.0003553606,0.0009284242,0.0004884769,0.008102986,0.1458435,0.01086676,0.1009546,0.7257184],"study_design_scores_gemma":[0.0002980427,0.0004588876,0.008349947,0.0001991778,0.0001949953,0.001275319,0.0004421386,0.4858072,0.260949,0.04433306,0.1973252,0.0003670296],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00206155,0.0001523877,0.9104258,0.0003123375,0.0000967575,0.000324596,0.005074362,0.08035017,0.001202078],"genre_scores_gemma":[0.03246679,0.0003163113,0.9371082,0.0003879818,0.0001224667,0.0011448,0.01849979,0.005162568,0.004791058],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01925997,"threshold_uncertainty_score":0.06443101,"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."}}