{"id":"W1570431431","doi":"10.1007/11735106_21","title":"A Hybrid Approach to Index Maintenance in Dynamic Text Retrieval Systems","year":2006,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Merge (version control); Computer science; Search engine indexing; Information retrieval; Data mining; Inverted index","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":[0.002574301,0.0007212816,0.002128912,0.003100421,0.001536945,0.00328532,0.004552845,0.001718163,0.004820659],"category_scores_gemma":[0.00753028,0.0009805762,0.001004782,0.004514755,0.001244606,0.006353538,0.002850709,0.001463885,0.001711503],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009561882,"about_ca_system_score_gemma":0.001297723,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003131198,"about_ca_topic_score_gemma":0.004733203,"domain_scores_codex":[0.9972984,0.0005587802,0.0003120303,0.0004336549,0.001216348,0.0001807183],"domain_scores_gemma":[0.9933129,0.002378964,0.00026556,0.002395045,0.001490251,0.0001572651],"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":[0.0007231503,0.00037983,0.001215187,0.0004355111,0.0002000437,0.0002002433,0.0004787663,0.06768502,0.03081787,0.02814749,0.01028259,0.8594343],"study_design_scores_gemma":[0.0001135646,0.0002956889,0.0006390117,0.00003062105,0.0001877138,0.0004686214,0.00012573,0.9293322,0.01727718,0.04011764,0.01133571,0.00007630659],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01465098,0.000935377,0.9792236,0.0002264735,0.0001148523,0.0001709685,0.0002018547,0.002941155,0.001534815],"genre_scores_gemma":[0.2130472,0.0005557114,0.7770634,0.0002348451,0.0002688924,0.0003724806,0.0008068879,0.0006669528,0.006983562],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004820659,"threshold_uncertainty_score":0.01612669,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009936672657317182,"score_gpt":0.2235050912192837,"score_spread":0.2135684185619665,"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."}}