{"id":"W2728728684","doi":"10.48550/arxiv.1707.00083","title":"Notes on Growing a Tree in a Graph","year":2017,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Graph Theory Research","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; University of Ottawa; University of British Columbia; McGill University","funders":"","keywords":"Combinatorics; Vertex (graph theory); Spanning tree; Graph; Mathematics; Enhanced Data Rates for GSM Evolution; Random graph; Tree (set theory); Discrete mathematics; Computer science; Artificial intelligence","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":[0.002222083,0.0009350815,0.001148663,0.001954634,0.002099802,0.002062249,0.002206933,0.002879092,0.0089576],"category_scores_gemma":[0.02122061,0.0005588263,0.001217235,0.00305406,0.003733465,0.005412349,0.002483591,0.003599197,0.001253869],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001511756,"about_ca_system_score_gemma":0.0007560148,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003558456,"about_ca_topic_score_gemma":0.002499408,"domain_scores_codex":[0.9986443,0.0003839294,0.00006603442,0.0003300424,0.0004311648,0.0001445079],"domain_scores_gemma":[0.9886437,0.008822056,0.000556295,0.0007217095,0.0005913921,0.0006649153],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002848572,0.00008716716,0.002215693,0.0005067731,0.00006487494,0.0006598892,0.0009910609,0.05868174,0.009541229,0.8521668,0.03491583,0.03988417],"study_design_scores_gemma":[0.00007256895,0.0001403132,0.001230543,0.00008662056,0.00005399294,0.0003365936,0.0002445699,0.09620082,0.002106227,0.8869486,0.01252861,0.00005059539],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3010113,0.01266062,0.5591277,0.02534319,0.00454759,0.0001658558,0.001817223,0.001780917,0.09354544],"genre_scores_gemma":[0.8128911,0.007103081,0.126485,0.004736921,0.004918629,0.0003792425,0.001824368,0.001485975,0.04017558],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0089576,"threshold_uncertainty_score":0.02996612,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1156148460602131,"score_gpt":0.2410833569852118,"score_spread":0.1254685109249987,"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."}}