{"id":"W3017971643","doi":"10.1016/j.mex.2020.100897","title":"Bayesian biclustering by dynamics: Algorithm testing, comparison against random agglomeration, and calculation of application specific prior information","year":2020,"lang":"en","type":"article","venue":"MethodsX","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund; University of Calgary","keywords":"Cluster analysis; Biclustering; Computer science; Bayesian probability; Algorithm; Series (stratigraphy); Data mining; Robustness (evolution); Bayesian inference; Artificial intelligence; Fuzzy clustering; Canopy clustering algorithm; Geology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005193994,0.0001083487,0.0002437063,0.00007257559,0.0001375441,0.0001482842,0.0001816841,0.00006195715,0.00000179505],"category_scores_gemma":[0.0001297976,0.0001058064,0.00003970701,0.0005730201,0.00003555228,0.0007623791,0.0001051469,0.0001031696,0.00000283195],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000311321,"about_ca_system_score_gemma":0.00001315831,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004215981,"about_ca_topic_score_gemma":0.000002558276,"domain_scores_codex":[0.9988558,0.0001049674,0.0005243782,0.0002086458,0.0001859055,0.0001203573],"domain_scores_gemma":[0.9990859,0.0001313998,0.0003874154,0.0001818637,0.00013941,0.00007398205],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000006125305,0.000007589402,0.0006611348,0.0000259542,0.000009970812,1.009015e-7,0.001366875,0.004389749,0.004317653,0.0002864111,0.00007815246,0.9888503],"study_design_scores_gemma":[0.0003821245,0.00003912166,0.0006470816,0.00001146261,0.000009161701,0.000001188894,0.00008477776,0.9931974,0.002412953,0.00009679776,0.003010251,0.0001076975],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003602569,0.000142521,0.9952794,0.0004020015,0.00003473821,0.0002089551,0.00000716522,0.00006045832,0.0002621728],"genre_scores_gemma":[0.2101094,0.00001113744,0.7896543,0.0001044812,0.000035316,0.000008931604,0.00006646962,0.000005135015,0.000004883014],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9888076,"threshold_uncertainty_score":0.4314658,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0200613903295541,"score_gpt":0.2574853852277157,"score_spread":0.2374239948981617,"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."}}