{"id":"W2598170113","doi":"10.1109/icsc.2017.80","title":"Querying Intimate-Core Groups in Weighted Graphs","year":2017,"lang":"en","type":"article","venue":"","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Core (optical fiber); Set (abstract data type); Theoretical computer science; Enhanced Data Rates for GSM Evolution; Node (physics); Graph; Heuristic; Artificial intelligence","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":[0.001931785,0.001197472,0.001503507,0.003761466,0.0009432668,0.001926615,0.002214692,0.002115457,0.001143278],"category_scores_gemma":[0.01360394,0.0005391655,0.001131116,0.005291366,0.0009319267,0.008089692,0.002213669,0.0009440698,0.0003128311],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00146967,"about_ca_system_score_gemma":0.0007806992,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00675144,"about_ca_topic_score_gemma":0.009435269,"domain_scores_codex":[0.9974155,0.0008240374,0.0001919059,0.0008557196,0.000538694,0.000174051],"domain_scores_gemma":[0.9935834,0.004032727,0.0009334033,0.0008964373,0.0003694585,0.0001845645],"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":[0.0008205643,0.0006134166,0.0166117,0.001311118,0.0003625923,0.0009416622,0.00167262,0.6318601,0.01843182,0.06535804,0.01126175,0.2507546],"study_design_scores_gemma":[0.00004541103,0.0000598187,0.00123343,0.0000237234,0.00006540972,0.000248428,0.0003606705,0.905839,0.003257142,0.08670152,0.002144793,0.00002067328],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3603055,0.001648017,0.6293435,0.001129457,0.00004575314,0.0003898159,0.002305785,0.001769877,0.003062268],"genre_scores_gemma":[0.7971882,0.0008193643,0.196926,0.0002458886,0.00006030532,0.0001713111,0.003179213,0.0001296889,0.001280007],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00675144,"threshold_uncertainty_score":0.01342428,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02228931397122379,"score_gpt":0.2940696360469692,"score_spread":0.2717803220757454,"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."}}