{"id":"W1966445797","doi":"10.1145/1620432.1620453","title":"Efficient keyword proximity search using a frontier-reduce strategy based on<i>d</i>-distance graph index","year":2009,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Graph; Set (abstract data type); Efficient frontier; Space (punctuation); Theoretical computer science; Index (typography); Frontier; World Wide Web","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000460401,0.0002153608,0.0001827983,0.0002398924,0.0002097751,0.0005210724,0.001264647,0.00004830364,0.00003280878],"category_scores_gemma":[0.00001044199,0.000185855,0.00008140146,0.0009967128,0.00005431358,0.0004186101,0.0001557742,0.0001981533,0.00003086005],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006830589,"about_ca_system_score_gemma":0.0001055776,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006191087,"about_ca_topic_score_gemma":0.00000576876,"domain_scores_codex":[0.997803,0.00007608627,0.0002340086,0.0006803122,0.0006707655,0.0005357691],"domain_scores_gemma":[0.9987564,0.00002770863,0.00006133841,0.000933442,0.00007663435,0.0001444613],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001070221,0.001981225,0.000992184,0.00005318109,0.0000404977,0.0001567329,0.0002332175,0.4524062,0.0005907956,0.2439745,0.006864917,0.2925995],"study_design_scores_gemma":[0.0004864099,0.0001354032,0.003546205,0.00002552915,0.000004319306,0.000001097642,0.00002564011,0.9924194,0.0005054341,0.002032571,0.0005625791,0.0002554747],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01866818,0.00002615677,0.9674481,0.0008563976,0.0002274109,0.0003558627,0.000006480552,0.0002540887,0.01215728],"genre_scores_gemma":[0.9177027,0.000001311836,0.08042408,0.0009144669,0.00005616251,0.000004886466,0.000006884725,0.000007562982,0.0008819415],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8990345,"threshold_uncertainty_score":0.7578946,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03283030230392023,"score_gpt":0.2784831588872907,"score_spread":0.2456528565833705,"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."}}