{"id":"W2554213973","doi":"10.5555/3192424.3192432","title":"Tradeoffs between density and size in extracting dense subgraphs: a unified framework","year":2016,"lang":"en","type":"article","venue":"Advances in Social Networks Analysis and Mining","topic":"Advanced Graph Theory Research","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Range (aeronautics); Maximization; Quadratic equation; Generalization; Theoretical computer science; Mathematical optimization; Algorithm; Mathematics","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":[0.001182067,0.0001825301,0.000513255,0.0002922165,0.0002783323,0.00009541255,0.0003148664,0.0001567912,0.000003895211],"category_scores_gemma":[0.0003617095,0.0001504879,0.00009878787,0.002777999,0.0002431582,0.000884432,0.0002153254,0.0003269976,2.282067e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004130398,"about_ca_system_score_gemma":0.00001649144,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002131493,"about_ca_topic_score_gemma":0.000601327,"domain_scores_codex":[0.9979426,0.0002944373,0.0003871799,0.0006097839,0.0002275928,0.0005383802],"domain_scores_gemma":[0.9958754,0.003574126,0.0001727008,0.0002294774,0.00004255735,0.0001057648],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002265338,0.00001487899,0.6803586,0.000005424247,0.00007160396,0.0000291032,0.001265269,0.00039366,0.00003616117,0.01343303,0.000001113075,0.3043684],"study_design_scores_gemma":[0.001049311,0.00007722554,0.8186008,0.0002585761,0.0001475666,0.000004603476,0.001520702,0.01578883,0.00006746293,0.1615176,0.0002959221,0.000671429],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5731259,0.002030222,0.4244656,0.0001779995,0.00003311353,0.00006506529,7.373851e-7,0.00002456808,0.00007678027],"genre_scores_gemma":[0.9740313,0.002095978,0.02364838,0.00005336113,0.0001315861,0.00001067203,6.911176e-7,0.00000962392,0.00001836815],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4009054,"threshold_uncertainty_score":0.6136717,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01822696647022869,"score_gpt":0.3146003937833189,"score_spread":0.2963734273130902,"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."}}