{"id":"W3214014018","doi":"10.15866/ireci.v2i2.16432","title":"Visually Mining Relational Data","year":2018,"lang":"en","type":"article","venue":"International Journal on Computer and Communications Networks Computational Intelligence and Data Analytics","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Exploit; Computer science; Clustering coefficient; Cluster analysis; Relational database; Data mining; Focus (optics); Zoom; Raw data; Metric (unit); Graph; Data science; Enhanced Data Rates for GSM Evolution; Theoretical computer science; Information retrieval; Artificial intelligence; Engineering","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":[0.001445825,0.001010824,0.0008655929,0.006678876,0.001039395,0.004831649,0.001495163,0.001095057,0.006211486],"category_scores_gemma":[0.01405445,0.0005970988,0.001155198,0.004724686,0.0007681879,0.005295343,0.003463641,0.001447731,0.002985524],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008244864,"about_ca_system_score_gemma":0.0007555853,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002257892,"about_ca_topic_score_gemma":0.003879717,"domain_scores_codex":[0.9981828,0.0004229549,0.0001341287,0.0004406793,0.0007204482,0.00009902145],"domain_scores_gemma":[0.9935941,0.003306482,0.0006677933,0.001050394,0.001174008,0.000207157],"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":[0.0005603169,0.0002614803,0.02118575,0.002932654,0.0003207455,0.002112796,0.006455423,0.02486158,0.03373635,0.07304506,0.07455707,0.7599707],"study_design_scores_gemma":[0.00007086292,0.0003018318,0.0137126,0.0007642832,0.0002634485,0.002790262,0.01128574,0.3547811,0.04101365,0.3352258,0.2396172,0.0001731425],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06857,0.002611465,0.8869178,0.00350477,0.0002918735,0.0005779487,0.01330082,0.009930667,0.01429471],"genre_scores_gemma":[0.2780076,0.002220587,0.7020168,0.0005947926,0.0001974785,0.0003408141,0.01151783,0.001019703,0.004084517],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006678876,"threshold_uncertainty_score":0.02077949,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1568110961614466,"score_gpt":0.4074227140897911,"score_spread":0.2506116179283445,"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."}}