{"id":"W2796721545","doi":"10.1101/285064","title":"Cluster Tendency Assessment in Neuronal Spike Data","year":2018,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; Toronto Rehabilitation Institute; University of Toronto","funders":"","keywords":"Cluster analysis; Computer science; Spike sorting; Visualization; Cluster (spacecraft); Initialization; Data mining; Sorting; Pattern recognition (psychology); Dimensionality reduction; Artificial intelligence; Ground truth; Spike (software development); Clustering high-dimensional data; Algorithm","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00360635,0.0004950893,0.0005983865,0.005293555,0.0004975926,0.001077666,0.0006552412,0.0006981362,0.001012702],"category_scores_gemma":[0.01408481,0.0001981876,0.0005346294,0.002744238,0.0006268062,0.001097367,0.00108371,0.0006798507,0.0002081741],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007049876,"about_ca_system_score_gemma":0.0003814577,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002201865,"about_ca_topic_score_gemma":0.001861855,"domain_scores_codex":[0.9985972,0.0004599696,0.0001445958,0.0003265635,0.00039884,0.00007278853],"domain_scores_gemma":[0.9879026,0.006722303,0.001220936,0.0009886728,0.002888131,0.0002773757],"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":[0.003265489,0.0003183444,0.09149002,0.0008061597,0.0006428682,0.0007568971,0.001951638,0.4812587,0.08203072,0.01241316,0.006201409,0.3188646],"study_design_scores_gemma":[0.00002344518,0.0001606603,0.02305247,0.00002812242,0.0000296598,0.0001989472,0.0002764024,0.9491671,0.01995901,0.006206366,0.0008273284,0.00007052689],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7172112,0.0002411599,0.278341,0.0001736013,0.00004668464,0.0001382637,0.001160552,0.001881282,0.0008062721],"genre_scores_gemma":[0.9119313,0.00004998428,0.0861056,0.00001878686,0.000009376627,0.00006366779,0.001392702,0.0001055396,0.0003230737],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005293555,"threshold_uncertainty_score":0.01907241,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04243097153292118,"score_gpt":0.2758749510079994,"score_spread":0.2334439794750782,"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."}}