{"id":"W4312158865","doi":"10.1016/j.isci.2022.105874","title":"Modeling foot sole cutaneous afferents: FootSim","year":2022,"lang":"en","type":"article","venue":"iScience","topic":"Tactile and Sensory Interactions","field":"Neuroscience","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"H2020 European Research Council; European Research Council; Medical Research Council; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; European Commission; Natural Sciences and Engineering Research Council of Canada; Horizon 2020 Framework Programme; Wellcome Trust","keywords":"Microneurography; Mechanoreceptor; Neuroscience; Afferent; Electrophysiology; Computer science; Functional electrical stimulation; Population; Sensory system; Stimulation; Biology; Medicine","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":[0.0001132696,0.0003488408,0.0002971616,0.0001977037,0.0001644394,0.0004351327,0.0007036331,0.000740975,0.005545797],"category_scores_gemma":[0.000375777,0.0002252241,0.0004003085,0.0001609001,0.0001411439,0.0002277476,0.0003444034,0.0003273726,0.0004380243],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001799986,"about_ca_system_score_gemma":0.0004887757,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00400261,"about_ca_topic_score_gemma":0.00477218,"domain_scores_codex":[0.9999634,0.000007189491,0.000002027934,0.000006766176,0.00001442965,0.000006190344],"domain_scores_gemma":[0.9998703,0.00006569432,0.00001537887,0.00001391657,0.00002181324,0.00001288583],"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":[0.00004813551,0.00003553751,0.0005229984,0.00007278574,0.00003269532,0.0001187602,0.00003847064,0.9729255,0.01503797,0.003094902,0.0003538605,0.007718259],"study_design_scores_gemma":[0.000005175094,0.00001605741,0.00008283839,0.000002797294,0.000006857801,0.00002494978,0.000005512753,0.9974167,0.00135476,0.0004434664,0.0006382291,0.000002524673],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1064103,0.000127294,0.8785339,0.0001918726,0.00009800097,0.00007658864,0.0005337643,0.001498772,0.01252944],"genre_scores_gemma":[0.8853607,0.0002491414,0.1020829,0.000157108,0.00003010392,0.0001336932,0.000300787,0.0002611906,0.01142432],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005545797,"threshold_uncertainty_score":0.01855254,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06554523568161806,"score_gpt":0.3009967731875894,"score_spread":0.2354515375059713,"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."}}