{"id":"W4400405332","doi":"10.1101/2024.07.04.602047","title":"Quantitative Analysis of Miniature Synaptic Calcium Transients Using Positive Unlabeled Deep Learning","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute; University of Toronto; Canadian Institute for Theoretical Astrophysics; Université Laval","funders":"","keywords":"Calcium; Neuroscience; Artificial intelligence; Psychology; Chemistry; Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003174873,0.000667445,0.001127473,0.0009528825,0.0001326011,0.00009023187,0.000354643,0.0006781418,0.00001572858],"category_scores_gemma":[0.0001300133,0.0007441085,0.0004453053,0.002262605,0.00009966484,0.0001206672,0.0002728999,0.002283254,0.00001169482],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003075144,"about_ca_system_score_gemma":0.0001034627,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001300456,"about_ca_topic_score_gemma":0.000002598709,"domain_scores_codex":[0.9973638,0.0001578442,0.0007081318,0.0008437072,0.0003513344,0.0005751692],"domain_scores_gemma":[0.9984296,0.0002181018,0.0002738694,0.0005454471,0.0003465611,0.0001864305],"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":[0.00002866221,0.00002501576,0.0002015244,0.0006778816,0.003007446,0.00008391825,0.00009905116,0.3919642,0.6037291,0.0001792832,0.000001697392,0.000002207948],"study_design_scores_gemma":[0.000298237,0.00007620532,0.005820997,0.0010502,0.004151134,3.084339e-8,0.0000257009,0.6509862,0.3366499,0.000003213573,0.00002912457,0.0009090376],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9665589,0.004717322,0.02647225,0.0000143183,0.001026917,0.0003726634,0.0001989069,0.0006315201,0.000007211984],"genre_scores_gemma":[0.9919103,0.0001113774,0.007644206,0.00002038459,0.0001136459,0.0000202706,0.000001397358,0.0001768994,0.000001554669],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2670792,"threshold_uncertainty_score":0.999501,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01910022098119249,"score_gpt":0.2549864273020907,"score_spread":0.2358862063208982,"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."}}