{"id":"W1594349942","doi":"10.1007/978-3-642-19668-3_12","title":"Uncertainty in Rank Join","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Join (topology); Ranking (information retrieval); Rank (graph theory); Result set; sort; Operator (biology); Set (abstract data type); Information retrieval; Domain (mathematical analysis); Aggregate (composite); Sort-merge join; Semantics (computer science); Theoretical computer science; Core (optical fiber); Learning to rank; Query optimization; Data mining; Programming language","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":["metaepi_narrow","open_science"],"consensus_categories":[],"category_scores_codex":[0.001209572,0.0005200372,0.0005297121,0.001312241,0.0001308069,0.000558008,0.005541276,0.0002365439,0.00006721245],"category_scores_gemma":[0.00005375055,0.0004715315,0.0001122719,0.0008611292,0.0005093298,0.001142094,0.002678894,0.0007811136,0.0001578466],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002445767,"about_ca_system_score_gemma":0.0002806962,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001268635,"about_ca_topic_score_gemma":0.000306274,"domain_scores_codex":[0.9960734,0.00003584349,0.0005477833,0.001708141,0.0008508292,0.0007840574],"domain_scores_gemma":[0.9975551,0.0001916359,0.0002215158,0.001787804,0.0001009913,0.000142928],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0000043666,0.00003399083,0.00004956896,0.00002632811,0.000007022241,0.000224535,0.0004858285,0.006710066,0.0000112049,0.06115858,0.0001011195,0.9311874],"study_design_scores_gemma":[0.0006183482,0.0001587609,0.0004015766,0.0004661476,0.000007346462,0.0000280299,1.619035e-7,0.4907392,0.0002046477,0.4914631,0.01479529,0.001117442],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00002263077,0.0002638747,0.977474,0.0005490149,0.001852967,0.0003783752,0.000005838626,0.0001443541,0.01930891],"genre_scores_gemma":[0.08059894,0.0003818569,0.9066018,0.00584951,0.001210056,0.00004138513,0.00004628457,0.0001083377,0.005161827],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9300699,"threshold_uncertainty_score":0.9998392,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02407417195732661,"score_gpt":0.2365362484956803,"score_spread":0.2124620765383536,"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."}}