{"id":"W2082662353","doi":"10.1152/ajpcell.00016.2012","title":"Automated region of interest analysis of dynamic Ca<sup>2+</sup> signals in image sequences","year":2012,"lang":"en","type":"article","venue":"American Journal of Physiology-Cell Physiology","topic":"Receptor Mechanisms and Signaling","field":"Biochemistry, Genetics and Molecular Biology","cited_by":64,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Montreal Heart Institute","funders":"National Center for Research Resources; National Heart, Lung, and Blood Institute; Canadian Institutes of Health Research","keywords":"Region of interest; Algorithm; Artificial intelligence; Noise (video); Computer science; SIGNAL (programming language); Amplitude; Computer vision; Physics; Pattern recognition (psychology); Image (mathematics); Optics","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":[0.002186364,0.00101254,0.000931459,0.001561831,0.0005062425,0.001729833,0.001396309,0.001032874,0.003756136],"category_scores_gemma":[0.006122529,0.0005423426,0.0007390227,0.001033623,0.0006189629,0.001015772,0.0007935892,0.001129399,0.001914793],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007164839,"about_ca_system_score_gemma":0.001470947,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001125899,"about_ca_topic_score_gemma":0.002002907,"domain_scores_codex":[0.9988397,0.0002258368,0.0001316872,0.0003507437,0.0003516049,0.0001004426],"domain_scores_gemma":[0.9973628,0.001068233,0.0003229979,0.0002680859,0.000893531,0.00008441049],"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":[0.0004291202,0.0001232974,0.001922524,0.0005639902,0.0001079312,0.0002740601,0.0004301116,0.01150312,0.634842,0.004873112,0.004928035,0.3400027],"study_design_scores_gemma":[0.00003833467,0.0002013904,0.00817636,0.00004924552,0.00006826664,0.00063328,0.0001175663,0.5320863,0.4395304,0.0032489,0.01575181,0.00009801488],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02072927,0.0001674558,0.9730682,0.00007848967,0.0000337361,0.0001747237,0.0002323963,0.004976775,0.000538971],"genre_scores_gemma":[0.04724862,0.0001475803,0.9502268,0.00005365683,0.0000147519,0.0004056042,0.0004517577,0.0007678777,0.0006833972],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003756136,"threshold_uncertainty_score":0.01256555,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01246533103744593,"score_gpt":0.2674489589365498,"score_spread":0.2549836278991038,"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."}}