{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002402213,0.001491115,0.001956275,0.00627069,0.001531694,0.003123249,0.002479974,0.001228863,0.007298548],"category_scores_gemma":[0.0154477,0.000838001,0.001177552,0.005835533,0.001228525,0.004159905,0.004044713,0.001988399,0.002506941],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001097176,"about_ca_system_score_gemma":0.002683324,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002469351,"about_ca_topic_score_gemma":0.004130742,"domain_scores_codex":[0.9954035,0.0006258845,0.000348058,0.0004569422,0.002906191,0.0002595003],"domain_scores_gemma":[0.9906889,0.003800943,0.0005354433,0.001703949,0.002881807,0.0003888088],"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":[0.0008876733,0.0003233031,0.007515627,0.000381653,0.0002357231,0.000205578,0.0003489428,0.06025642,0.0150298,0.04300999,0.01822136,0.8535838],"study_design_scores_gemma":[0.00009281249,0.0002302134,0.002007308,0.00005357874,0.00009115059,0.0003766025,0.0002453628,0.9000005,0.01759457,0.06703015,0.01221467,0.00006307233],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0188181,0.0004774763,0.973612,0.0001931703,0.0001620558,0.0001830461,0.0005279702,0.004251319,0.001774777],"genre_scores_gemma":[0.1254878,0.0002440201,0.8683389,0.00008932427,0.0001704233,0.0003601409,0.001930417,0.0004952567,0.002883722],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007298548,"threshold_uncertainty_score":0.02441609,"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."}}