{"id":"W2900653001","doi":"10.1145/3274895.3276476","title":"Using biconnected components for efficient identification of upstream features in large spatial networks (GIS cup)","year":2018,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Computer science; Upstream (networking); Laptop; Graph; Identification (biology); Data mining; Theoretical computer science; Computer network","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009387361,0.002239159,0.001385733,0.005686888,0.001846305,0.001834964,0.001968505,0.00155744,0.00365665],"category_scores_gemma":[0.007384148,0.001154354,0.0009676648,0.006630096,0.001151224,0.00330638,0.002758064,0.001328668,0.001082028],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001419903,"about_ca_system_score_gemma":0.002781564,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04506283,"about_ca_topic_score_gemma":0.06783119,"domain_scores_codex":[0.9989625,0.0002494857,0.00005534806,0.000280918,0.0002953706,0.0001563592],"domain_scores_gemma":[0.997704,0.001050067,0.0003016942,0.0003060567,0.0004746257,0.0001635219],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00053845,0.0003208144,0.01161426,0.0007933826,0.0002222143,0.0005439716,0.001205086,0.5003557,0.01004816,0.03433952,0.01720148,0.4228171],"study_design_scores_gemma":[0.00003630445,0.00005473455,0.001236125,0.00005720076,0.00004739329,0.0001443665,0.0003508111,0.9592062,0.00309295,0.03050235,0.005236865,0.00003469799],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06890453,0.0007278731,0.9217414,0.0003986586,0.00004237986,0.0004393512,0.001052358,0.003279877,0.003413681],"genre_scores_gemma":[0.2178845,0.000332459,0.7750718,0.0001315293,0.00002171831,0.0002910534,0.003216373,0.0003635961,0.002686937],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04506283,"threshold_uncertainty_score":0.0896011,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02628290578341722,"score_gpt":0.2841490783017879,"score_spread":0.2578661725183706,"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."}}