{"id":"W2018198263","doi":"10.1080/10618600.2013.837828","title":"The Conditional-Potts Clustering Model","year":2013,"lang":"en","type":"article","venue":"Journal of Computational and Graphical Statistics","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Cluster analysis; Mathematics; Adjacency list; Posterior probability; Bayesian inference; Correlation clustering; Computer science; Algorithm; Bayesian probability; Statistics","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":[],"consensus_categories":[],"category_scores_codex":[0.00009733869,0.00005158017,0.00005740515,0.00002587352,0.0001282857,0.00004718111,0.00006965896,0.00003421572,0.000008302644],"category_scores_gemma":[0.00005371703,0.00003353897,0.00002972184,0.00003809857,0.00009068225,0.000005106868,0.00002365988,0.00007152081,0.000001393861],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000367058,"about_ca_system_score_gemma":0.00004912014,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":8.119954e-7,"about_ca_topic_score_gemma":0.000001474981,"domain_scores_codex":[0.9994608,0.00002643964,0.0002119579,0.00006370885,0.0001690752,0.00006800565],"domain_scores_gemma":[0.9993796,0.00006415089,0.0001338021,0.00004100625,0.0003098678,0.00007156753],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003378101,0.0002240682,0.003512804,0.00005278205,0.0003030104,0.000006673868,0.0001305707,0.35638,0.05782501,0.205865,0.2857303,0.08963203],"study_design_scores_gemma":[0.0007181714,0.000242484,0.07619515,0.00001390827,0.0000188152,0.00009504526,0.00006098787,0.2793194,0.0003285151,0.6284047,0.01446462,0.000138238],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08289775,0.0003329417,0.9151035,0.001431251,0.00007878478,0.00004874537,0.00002238018,0.000001367134,0.00008328944],"genre_scores_gemma":[0.9803034,0.0002329196,0.01883851,0.0003631405,0.0000977965,0.000003924222,0.00002985483,0.000004320014,0.0001261391],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8974056,"threshold_uncertainty_score":0.136768,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009403747627858285,"score_gpt":0.2532570640417436,"score_spread":0.2438533164138853,"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."}}