{"id":"W2044998619","doi":"10.1016/j.bbamcr.2014.11.002","title":"Quantitative analysis of mitochondrial morphology and membrane potential in living cells using high-content imaging, machine learning, and morphological binning","year":2014,"lang":"en","type":"article","venue":"Biochimica et Biophysica Acta (BBA) - Molecular Cell Research","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":152,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Center for Advancing Translational Sciences; National Center for Research Resources; National Institute of General Medical Sciences; National Eye Institute; Ministry of Patriots and Veterans Affairs; U.S. Public Health Service; Foundation Fighting Blindness; Arnold and Mabel Beckman Foundation; National Institute of Environmental Health Sciences; U.S. Department of Veterans Affairs","keywords":"Oligomycin; Mitochondrion; Membrane potential; Biophysics; Cell biology; Inner mitochondrial membrane; Fluorescence microscope; Rotenone; Antimycin A; Biology; Chemistry; Biochemistry; Fluorescence; Physics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001034066,0.0003286492,0.0007021768,0.0008172085,0.0001715895,0.00007504893,0.0002826808,0.0001939856,0.00002086603],"category_scores_gemma":[0.0003812189,0.0003037193,0.0001980442,0.0008292258,0.0006521235,0.00001238089,0.0008385467,0.0005136005,0.000001122649],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000241306,"about_ca_system_score_gemma":0.00005254671,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000802091,"about_ca_topic_score_gemma":0.00003764348,"domain_scores_codex":[0.9964826,0.001059312,0.0004621275,0.000964393,0.0004080317,0.0006235395],"domain_scores_gemma":[0.9988353,0.0001995526,0.0002315748,0.000368877,0.0002138843,0.0001508234],"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":[0.0002528983,0.0001984768,0.0004153073,0.0000394118,0.0004006202,0.00002966136,0.0001201472,0.0001926875,0.9980204,0.0002213724,0.0000162394,0.00009274117],"study_design_scores_gemma":[0.0007973196,0.0007168206,0.003353364,0.00002294297,0.0002755471,0.000006779758,0.0002058148,0.01613216,0.978051,0.00005412246,0.00007414679,0.000309955],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9958475,0.001353604,0.00205175,0.0001981807,0.00008048386,0.0002864502,0.00003566001,0.00000970046,0.0001367152],"genre_scores_gemma":[0.991671,0.001659804,0.006333932,0.00006607427,0.00005672246,0.00001597951,0.00008146759,0.00003966431,0.0000753273],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01996941,"threshold_uncertainty_score":0.9999415,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02913039821623398,"score_gpt":0.3110376686494546,"score_spread":0.2819072704332207,"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."}}