{"id":"W2495876702","doi":"10.1016/j.tcs.2016.07.018","title":"An efficient method to evaluate intersections on big data sets","year":2016,"lang":"en","type":"article","venue":"Theoretical Computer Science","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Winnipeg","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Trie; Intersection (aeronautics); Identifier; Computer science; Context (archaeology); Set (abstract data type); Interval (graph theory); Theoretical computer science; Tree (set theory); Search tree; Sequence (biology); Data structure; Node (physics); Inverted index; Algorithm; Binary tree; Data mining; Mathematics; Information retrieval; Search algorithm; Search engine indexing; Combinatorics","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":"codex-gemma-dda1882f352a","candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.003668713,0.0002103798,0.0001916477,0.0003077536,0.0005010925,0.0004940461,0.007630551,0.00004267968,0.00004016235],"category_scores_gemma":[0.0001512753,0.0001215594,0.00003597982,0.001140314,0.0007556085,0.0007781297,0.004837737,0.0001367927,0.0003944221],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008523041,"about_ca_system_score_gemma":0.0001392657,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008381557,"about_ca_topic_score_gemma":0.000001075881,"domain_scores_codex":[0.9961554,0.0003289462,0.0002697328,0.001596689,0.001033184,0.0006160812],"domain_scores_gemma":[0.9947888,0.0004426741,0.00005600717,0.003897914,0.0001972003,0.0006173722],"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":[0.00001029939,0.00009304376,0.000003462271,8.573631e-7,0.000001533502,0.000005269985,0.0001391362,0.0007400631,0.00214184,0.4133938,0.0002216775,0.583249],"study_design_scores_gemma":[0.0002423739,0.0006029518,0.0006656849,0.00007573508,0.000003920476,0.00003055767,0.000003928413,0.970352,0.004507086,0.02234679,0.0009179348,0.0002510629],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009710053,0.00000335916,0.9849219,0.002478337,0.001794069,0.0002017023,0.00003027842,0.00025023,0.0006100453],"genre_scores_gemma":[0.5677077,5.843297e-7,0.431309,0.0007928982,0.0001714536,0.000005240973,0.000001829365,0.000006244721,0.000005040553],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9696119,"threshold_uncertainty_score":0.9977387,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0577306352145133,"score_gpt":0.3662562790384329,"score_spread":0.3085256438239196,"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."}}