{"id":"W2585936885","doi":"10.1145/3007186","title":"Inverted Treaps","year":2017,"lang":"en","type":"article","venue":"ACM Transactions on Information Systems","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Inverted index; Merge (version control); Identifier; ENCODE; Intersection (aeronautics); Thresholding; Representation (politics); Index (typography); Data mining; Information retrieval; Search engine indexing; Theoretical computer science; Artificial intelligence; Image (mathematics)","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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0001731427,0.0001042665,0.0001150329,0.0001434672,0.0009365194,0.001170458,0.001574842,0.00006885969,0.00001484321],"category_scores_gemma":[0.00003218886,0.00008679677,0.00004986962,0.00008658378,0.00002663788,0.006682963,0.00003082283,0.0001154518,0.0006995884],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003652773,"about_ca_system_score_gemma":0.00003643594,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002884751,"about_ca_topic_score_gemma":0.000004368301,"domain_scores_codex":[0.9991227,0.0000256594,0.0002931577,0.0001197056,0.0002922065,0.0001466042],"domain_scores_gemma":[0.997525,0.00003990507,0.0002240615,0.002026275,0.0001022555,0.00008248792],"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":[0.00001894363,0.00007892178,0.00008379066,0.00006206226,0.00005047183,0.000003001307,0.001683896,0.003818793,0.00004763094,0.0208862,0.006263425,0.9670029],"study_design_scores_gemma":[0.001606205,0.0001590369,0.004204226,0.0001775832,0.00001351073,0.0000616269,0.0003291585,0.588111,0.001202042,0.0007930871,0.4028549,0.000487555],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0004242009,0.000009493291,0.9902737,0.0004993499,0.00148482,0.0001770071,0.00003073877,0.0001969808,0.006903692],"genre_scores_gemma":[0.9906826,0.00002199013,0.008570081,0.0002013326,0.00004355449,0.00006015666,0.0000172373,0.000004245075,0.000398809],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9902584,"threshold_uncertainty_score":0.9998664,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02721772894203236,"score_gpt":0.2607516944946491,"score_spread":0.2335339655526167,"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."}}