{"id":"W1490473477","doi":"10.1007/3-540-44808-x_7","title":"Experiments on Adaptive Set Intersections for Text Retrieval Systems","year":2001,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":70,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of New Brunswick; University of Waterloo","funders":"","keywords":"Computer science; Intersection (aeronautics); Set (abstract data type); Factor (programming language); Algorithm; Measure (data warehouse); Theoretical computer science; Data mining","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":[0.004561859,0.001265812,0.002619523,0.002094231,0.001874068,0.001779039,0.002543967,0.001692776,0.009762547],"category_scores_gemma":[0.03276413,0.0008475403,0.000963793,0.003376197,0.0009053879,0.004246064,0.002002334,0.001397242,0.002676192],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001225803,"about_ca_system_score_gemma":0.001176251,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005319709,"about_ca_topic_score_gemma":0.002872808,"domain_scores_codex":[0.9931298,0.002838328,0.001014817,0.0007659954,0.001758001,0.0004930826],"domain_scores_gemma":[0.9465094,0.04186928,0.001142698,0.004092418,0.00552165,0.0008645366],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.04886555,0.01278097,0.009982395,0.00494097,0.001219194,0.000707124,0.002297622,0.2203933,0.1123041,0.007156982,0.01840081,0.560951],"study_design_scores_gemma":[0.003575756,0.01630918,0.008371823,0.0001309033,0.0009664622,0.0006834763,0.001534342,0.799854,0.1497308,0.007604363,0.01099378,0.0002450846],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.959784,0.002522724,0.02731529,0.0002706507,0.0002753418,0.0005778678,0.001412025,0.00286348,0.004978586],"genre_scores_gemma":[0.919875,0.0008854773,0.06724279,0.0001131185,0.0001770358,0.0004634462,0.005205934,0.0005436661,0.005493484],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009762547,"threshold_uncertainty_score":0.03265899,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04321719752391397,"score_gpt":0.2900568023509737,"score_spread":0.2468396048270597,"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."}}