{"id":"W4385336401","doi":"10.1016/j.mbs.2023.109053","title":"A multi-scale simulation of retinal physiology","year":2023,"lang":"en","type":"article","venue":"Mathematical Biosciences","topic":"Neuroscience and Neural Engineering","field":"Neuroscience","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Ontario","keywords":"Coupling (piping); Visual phototransduction; Retinal; Computer science; Retina; Discretization; Photoreceptor cell; Computer simulation; Biological system; Mathematical analysis; Physics; Mathematics; Optics; Simulation; Biology; Materials science","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":[0.0002929659,0.0003675727,0.0007747687,0.0004726161,0.000651556,0.001125289,0.001018812,0.002202723,0.003255586],"category_scores_gemma":[0.002269363,0.0004587809,0.0008267078,0.0004789953,0.001093678,0.0008815427,0.001264533,0.001029328,0.0002493506],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009366749,"about_ca_system_score_gemma":0.0009340228,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01683471,"about_ca_topic_score_gemma":0.006911008,"domain_scores_codex":[0.9998775,0.00003924443,0.000006301671,0.00002239127,0.000035411,0.00001921171],"domain_scores_gemma":[0.9992288,0.0004785579,0.00005970139,0.0000603384,0.00007476429,0.00009773074],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002469313,0.00002751808,0.0004418411,0.00001811228,0.0000189626,0.00006572349,0.00003943472,0.9829658,0.001280496,0.01348394,0.0003090595,0.001324575],"study_design_scores_gemma":[0.000006859689,0.000003590618,0.00008638967,0.000001418026,0.000002005613,0.000005477837,0.000004429856,0.9983109,0.00005355536,0.00140988,0.0001130332,0.000002550805],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4439594,0.0008476378,0.5029343,0.003747151,0.0004768704,0.0001385331,0.0006370863,0.0009182148,0.04634086],"genre_scores_gemma":[0.9585574,0.0002589432,0.03643751,0.0002604111,0.00007102133,0.0001002877,0.0001102905,0.0001143524,0.004089797],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01683471,"threshold_uncertainty_score":0.03347343,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08929894797866872,"score_gpt":0.3362789799451835,"score_spread":0.2469800319665147,"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."}}