{"id":"W4402567765","doi":"10.1085/jgp.202413623","title":"Using neural networks for image analysis in general physiology","year":2024,"lang":"en","type":"article","venue":"The Journal of General Physiology","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Center for Research Resources; Hospital for Sick Children; National Institute of Arthritis and Musculoskeletal and Skin Diseases; Rush University","keywords":"Computer science; Convolutional neural network; CLARITY; TRACE (psycholinguistics); Set (abstract data type); Artificial intelligence; Code (set theory); Artificial neural network; Cognitive science; Machine learning; Programming language; Biology; Psychology","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":[],"consensus_categories":[],"category_scores_codex":[0.0006300689,0.0002131209,0.000507269,0.0002890727,0.00006899526,0.00002849852,0.000438675,0.0001684915,0.00002582902],"category_scores_gemma":[0.00004319338,0.0001446845,0.0006116588,0.0005076241,0.0001790538,0.00001567115,0.000143921,0.0002757556,8.289202e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003585587,"about_ca_system_score_gemma":0.00005990503,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008675355,"about_ca_topic_score_gemma":0.00006235272,"domain_scores_codex":[0.9983593,0.0003567139,0.0005510442,0.0002787316,0.0000894449,0.0003648178],"domain_scores_gemma":[0.9991061,0.00006666741,0.0002392675,0.000365028,0.0001685751,0.0000543763],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002214981,0.00003177676,0.00005189341,0.00001071069,0.0006701759,0.000006802278,0.00002309881,0.1228291,0.8728428,0.00002756518,0.002758892,0.0005257562],"study_design_scores_gemma":[0.0004729531,0.0005681208,0.004460549,0.00001218048,0.001315393,0.0001242498,0.00003238038,0.8001743,0.1890802,0.001236181,0.002222172,0.0003013863],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9629931,0.002697766,0.03382299,0.0001485816,0.0001548253,0.0001287377,0.000004752184,0.000009607456,0.00003962716],"genre_scores_gemma":[0.9927224,0.0006015143,0.004151492,0.0004766109,0.001757606,0.000006514394,0.00006163336,0.00003113009,0.0001911034],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6837626,"threshold_uncertainty_score":0.5900064,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01353786085886963,"score_gpt":0.3111537732689054,"score_spread":0.2976159124100358,"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."}}