{"id":"W2783397217","doi":"10.46430/phen0074","title":"Dealing with Big Data and Network Analysis Using Neo4j","year":2018,"lang":"en","type":"article","venue":"The Programming Historian","topic":"Graph Theory and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Graph database; Graph; Focus (optics); Big data; Power graph analysis; Data science; Information retrieval; Theoretical computer science; Data mining; Physics","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":[0.0008671383,0.0001172308,0.0001521073,0.000083724,0.0006977117,0.0001987074,0.00110025,0.00003006838,0.000002098621],"category_scores_gemma":[0.00001223444,0.00007812516,0.00003165416,0.0009603534,0.0002473636,0.0002015738,0.0004015095,0.0001008057,0.000003064172],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002883789,"about_ca_system_score_gemma":0.00003479427,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006370521,"about_ca_topic_score_gemma":0.0001937662,"domain_scores_codex":[0.99897,0.00006322183,0.0001297452,0.0003847617,0.0001628251,0.0002894573],"domain_scores_gemma":[0.9985106,0.00005742241,0.0001023853,0.001194591,0.00005572364,0.00007924989],"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":[0.00002259098,0.0000243666,0.001551303,0.00000684569,0.0004536053,0.00001709364,0.004900503,0.0003485717,0.00003818607,0.07377509,0.0000508983,0.918811],"study_design_scores_gemma":[0.0005176943,0.0004033305,0.0003918199,0.00007067137,0.00127355,0.0000998043,0.0002162253,0.5131934,0.00005409301,0.03244584,0.4505687,0.0007648806],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008243568,0.0005197389,0.9895267,0.0001427423,0.0006665075,0.0001177473,0.000001085786,0.0001396317,0.0006422963],"genre_scores_gemma":[0.7178707,0.000003539189,0.2804309,0.00008248229,0.001279897,0.000003067894,0.000003977484,0.0000129307,0.0003124818],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9180461,"threshold_uncertainty_score":0.5366305,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04419729533468023,"score_gpt":0.2586589628975906,"score_spread":0.2144616675629104,"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."}}