{"id":"W2118800699","doi":"10.1186/gb-2005-6-13-r115","title":"iVici: Interrelational Visualization and Correlation Interface","year":2005,"lang":"en","type":"article","venue":"Genome biology","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Genome Canada","keywords":"Visualization; Computer science; Overlay; Correlation; Set (abstract data type); Interface (matter); Data mining; Information visualization; Computational biology; Theoretical computer science; Biology; Mathematics","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":[0.0001194943,0.00009378697,0.00008103299,0.00003796664,0.00005658067,0.00001314532,0.00007131708,0.0001684213,0.00005835449],"category_scores_gemma":[0.00001500305,0.00008926792,0.00002518712,0.00003598358,0.00006644108,0.000004898195,0.00009396207,0.00005716444,0.00004566342],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001385544,"about_ca_system_score_gemma":0.00001772481,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002503245,"about_ca_topic_score_gemma":0.00001678249,"domain_scores_codex":[0.9994424,0.00002743152,0.0001912005,0.0001711181,0.00002684411,0.0001410096],"domain_scores_gemma":[0.999725,0.000008049041,0.00007417547,0.0001152477,0.00003439614,0.00004307092],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004089292,0.0001797681,0.02868299,0.00005083122,0.0003532849,6.801786e-7,0.002096863,0.01098821,0.69098,0.09206621,0.005985864,0.1682064],"study_design_scores_gemma":[0.00154827,0.0008055402,0.02221154,0.00001526112,0.00003669617,0.00008756618,0.0001858529,0.06602869,0.004236375,0.004173052,0.900002,0.0006691627],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5471725,0.002525842,0.4461409,0.0003024103,0.0002530234,0.0001706656,0.00002043453,0.00001856572,0.003395654],"genre_scores_gemma":[0.9968719,0.0001390413,0.001189188,0.0004292571,0.0003632162,0.000006115974,0.0004356858,0.0000093761,0.000556219],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8940161,"threshold_uncertainty_score":0.364024,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006808359511202564,"score_gpt":0.2543127080351668,"score_spread":0.2475043485239642,"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."}}