{"id":"W2953722276","doi":"10.1016/j.ipm.2019.102063","title":"A multi-centrality index for graph-based keyword extraction","year":2019,"lang":"en","type":"article","venue":"Information Processing & Management","topic":"Advanced Text Analysis Techniques","field":"Computer Science","cited_by":92,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University","funders":"Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Centrality; Betweenness centrality; PageRank; Computer science; Clustering coefficient; Cluster analysis; Graph; Artificial intelligence; Data mining; Natural language processing; Information retrieval; Theoretical computer science; Mathematics; Statistics","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":[0.0003660819,0.0001375734,0.0001294557,0.0003616163,0.0001403117,0.0003951232,0.0004776535,0.00004876306,0.000008091645],"category_scores_gemma":[0.00001367955,0.0001347611,0.0000826362,0.0005614907,0.0000172716,0.004635443,0.00008658486,0.00007922481,0.00005093828],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001144923,"about_ca_system_score_gemma":0.00003104425,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005427016,"about_ca_topic_score_gemma":0.000002081643,"domain_scores_codex":[0.9988089,0.00001515004,0.0004047324,0.0002085057,0.0003219728,0.0002407078],"domain_scores_gemma":[0.9989888,0.00001918225,0.0003598099,0.0003994864,0.0001891275,0.00004356863],"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":[0.00002568861,0.0001182913,0.001095972,0.0006030032,0.00002926549,5.37352e-7,0.0003496749,0.009555063,0.0000794947,0.009800004,0.0004027241,0.9779403],"study_design_scores_gemma":[0.001078959,0.00003381771,0.007995318,0.00008780326,0.00001919952,7.755364e-7,0.0000922242,0.9403892,0.001364928,0.005125605,0.04354413,0.0002680633],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001378752,0.00001598634,0.9954499,0.0002982869,0.0001115375,0.0007560275,0.00000124272,0.0005685277,0.001419768],"genre_scores_gemma":[0.5957311,0.000003641267,0.4033107,0.0006041412,0.000006837212,0.0001370085,0.00002099374,0.000004647972,0.0001810093],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9776722,"threshold_uncertainty_score":0.5495397,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01397295543473869,"score_gpt":0.2989504196007305,"score_spread":0.2849774641659918,"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."}}