{"id":"W3094173950","doi":"10.1109/tvcg.2020.3032984","title":"Understanding Missing Links in Bipartite Networks With MissBiN","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Visualization and Computer Graphics","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Computer science; Bipartite graph; Variety (cybernetics); Link analysis; Task (project management); Visualization; Data mining; Intelligence analysis; Machine learning; Link (geometry); Informatics; Artificial intelligence; Information retrieval; Data science; Theoretical computer science","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":[0.0001368904,0.0001854593,0.0001868092,0.0002841133,0.000218278,0.0003694925,0.0002356216,0.0001316068,0.000009903292],"category_scores_gemma":[0.000002053414,0.0001763799,0.00004179042,0.001679388,0.00006615406,0.0004535326,0.000005988656,0.0002871248,0.000003090086],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002974763,"about_ca_system_score_gemma":0.00004563864,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005776217,"about_ca_topic_score_gemma":0.0000265626,"domain_scores_codex":[0.9987203,0.000105202,0.0002901778,0.0004233763,0.0002435123,0.0002173887],"domain_scores_gemma":[0.9993979,0.00006915676,0.00008251875,0.0001940693,0.00005544089,0.0002009015],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005012074,0.0002709818,0.000582641,0.00007100915,0.0000609551,0.00003102421,0.003138773,0.05822092,0.00002148292,0.9325837,0.0004893502,0.004479063],"study_design_scores_gemma":[0.0006524342,0.0002038278,0.00006661694,0.00008220165,0.00001223618,0.000007159798,0.00005697075,0.9975432,0.0002946436,0.0002283805,0.000626849,0.0002255166],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0005230772,0.00002439544,0.9981042,0.0008046766,0.0001496377,0.0001290358,0.000003107208,0.0002125287,0.00004939478],"genre_scores_gemma":[0.9904238,0.0001991269,0.002518702,0.006767788,0.00004673021,0.000004565141,0.000010717,0.00002031042,0.000008287094],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9955854,"threshold_uncertainty_score":0.7192563,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07947087621210688,"score_gpt":0.2855767233914348,"score_spread":0.2061058471793279,"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."}}