{"id":"W3043303392","doi":"10.3389/fncir.2020.00042","title":"Automated Curation of CNMF-E-Extracted ROI Spatial Footprints and Calcium Traces Using Open-Source AutoML Tools","year":2020,"lang":"en","type":"article","venue":"Frontiers in Neural Circuits","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Advanced Research; Vector Institute; Hospital for Sick Children; University of Toronto","funders":"Canadian Institutes of Health Research; National Institutes of Health; Hospital for Sick Children; Compute Canada; Natural Sciences and Engineering Research Council of Canada; Fondation Brain Canada; National Institute of Mental Health; Canadian Institute for Advanced Research","keywords":"Computer science; Classifier (UML); Artificial intelligence; Calcium imaging; Ground truth; Scalability; Spotting; Data curation; Pattern recognition (psychology); Machine learning; Data mining; Calcium; Database; Chemistry","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003873994,0.002236281,0.001651775,0.003729,0.001268123,0.002337377,0.002263936,0.001536759,0.01862508],"category_scores_gemma":[0.0145253,0.00112746,0.001673049,0.001342042,0.0008649353,0.001801982,0.002575281,0.002118498,0.01562728],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001061211,"about_ca_system_score_gemma":0.002742608,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003469065,"about_ca_topic_score_gemma":0.01061909,"domain_scores_codex":[0.9984535,0.0001807219,0.0001488259,0.00052771,0.0005500044,0.0001392979],"domain_scores_gemma":[0.9939821,0.002416862,0.0005103423,0.001318146,0.001566359,0.0002061917],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006045167,0.0001546702,0.007450941,0.002678509,0.0003166295,0.001191096,0.001039996,0.01010627,0.2882774,0.008154717,0.2081313,0.4718938],"study_design_scores_gemma":[0.0001580398,0.0001936281,0.01687611,0.0005635718,0.0001477337,0.001880859,0.0006037985,0.2579435,0.4539399,0.02595679,0.2414286,0.0003073921],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0096134,0.0004100978,0.8053318,0.0002734238,0.0001953127,0.0002820034,0.008829714,0.1732146,0.001849639],"genre_scores_gemma":[0.04158245,0.0003135924,0.9094154,0.0004757984,0.0000656443,0.001248676,0.01650208,0.02691848,0.003477877],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01862508,"threshold_uncertainty_score":0.06230712,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08869694879227998,"score_gpt":0.295619780264923,"score_spread":0.206922831472643,"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."}}