{"id":"W2154159756","doi":"10.1111/jmi.12322","title":"An automated cell viability quantification method for low‐resolution confocal images of closely packed cells based on a modified gradient flow tracking algorithm","year":2015,"lang":"en","type":"article","venue":"Journal of Microscopy","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; McGill University; Polytechnique Montréal; Centre Hospitalier Universitaire Sainte-Justine","funders":"Canadian Institutes of Health Research","keywords":"Confocal; Resolution (logic); Tracking (education); Confocal microscopy; Biological system; Fluorescence; Packed bed; Computer science; Fluorescence microscope; Image resolution; Artificial intelligence; Computer vision; Chromatography; Chemistry; Optics; Physics; Biology","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":[0.0006716119,0.0008224577,0.0009183673,0.001591399,0.0006643216,0.000828899,0.00133544,0.000958112,0.001323639],"category_scores_gemma":[0.0009883821,0.0004716981,0.0006189163,0.0007050463,0.0004233463,0.000795615,0.0005400641,0.001024357,0.0008396312],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007299366,"about_ca_system_score_gemma":0.001105124,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002252651,"about_ca_topic_score_gemma":0.002774819,"domain_scores_codex":[0.9993057,0.00008807964,0.00003711205,0.0001722182,0.0003566744,0.00004018847],"domain_scores_gemma":[0.9993871,0.0001433252,0.00007515111,0.00006110703,0.0003069121,0.00002629542],"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":[0.0001223438,0.0001086813,0.001522932,0.0002309723,0.00005903057,0.0001244049,0.000107554,0.01484675,0.6445014,0.003787305,0.002686351,0.3319023],"study_design_scores_gemma":[0.00004091475,0.0001754845,0.003699609,0.00002748068,0.00005269836,0.0007695397,0.00002294532,0.5984792,0.380999,0.001423306,0.01416698,0.0001428971],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0111155,0.0002155488,0.9864509,0.00004187049,0.0000429832,0.0000772467,0.0000545132,0.001667434,0.0003338701],"genre_scores_gemma":[0.03127504,0.0001847103,0.9669529,0.00003942541,0.00002052656,0.0002164838,0.000177045,0.0001006975,0.001033185],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002252651,"threshold_uncertainty_score":0.005296052,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01831010335134685,"score_gpt":0.3467226438359266,"score_spread":0.3284125404845797,"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."}}