{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004809602,0.001113286,0.001055994,0.002836322,0.001434069,0.006945315,0.004684365,0.001483102,0.02435246],"category_scores_gemma":[0.01681741,0.001336421,0.002373019,0.00445562,0.001776829,0.01423972,0.005346245,0.005125489,0.01072861],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001835587,"about_ca_system_score_gemma":0.002565115,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004992606,"about_ca_topic_score_gemma":0.008904614,"domain_scores_codex":[0.997061,0.0004906767,0.0002800492,0.000393693,0.001624825,0.0001497847],"domain_scores_gemma":[0.993605,0.002885495,0.0002359247,0.001777754,0.001186963,0.0003088693],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001404828,0.00009191864,0.001864243,0.0009147514,0.0001049266,0.0005083552,0.0009813345,0.009736629,0.003288249,0.4547327,0.2524603,0.275176],"study_design_scores_gemma":[0.00002588324,0.00001836651,0.0004507783,0.0002244286,0.00003655299,0.0006395372,0.00028148,0.04402218,0.004049624,0.4818282,0.4683607,0.00006233113],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001732047,0.0008600163,0.9467037,0.003974302,0.0006547072,0.000100146,0.002157494,0.02363318,0.02018442],"genre_scores_gemma":[0.02315899,0.002598169,0.9326879,0.00304057,0.0003310497,0.0003445841,0.005922935,0.01072719,0.02118855],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02435246,"threshold_uncertainty_score":0.08146709,"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."}}