{"id":"W2763926089","doi":"10.48550/arxiv.1710.02058","title":"Skyline Computation with Noisy Comparisons","year":2017,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Skyline; Computer science; Computation; Artificial intelligence; Data mining; Algorithm","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"],"consensus_categories":[],"category_scores_codex":[0.0001819836,0.00028384,0.0003107756,0.0002157439,0.0003141232,0.0005445674,0.002865432,0.0001194014,0.00001003565],"category_scores_gemma":[0.000009113599,0.0002900937,0.0000955883,0.0002321378,0.0001296033,0.000857464,0.003026544,0.0003988777,0.0001446483],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007955508,"about_ca_system_score_gemma":0.0001031232,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001850734,"about_ca_topic_score_gemma":0.00009479874,"domain_scores_codex":[0.9983858,0.00005940311,0.0001397452,0.001003269,0.0001208196,0.0002909889],"domain_scores_gemma":[0.9977405,0.00003945009,0.0003701528,0.001592579,0.0001332184,0.0001240667],"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":[0.00006420515,0.0004171602,0.008919976,0.0002162181,0.000465752,0.001521247,0.000231725,0.5877348,0.000004407806,0.3683032,0.01937601,0.01274526],"study_design_scores_gemma":[0.00061583,0.00006112583,0.003933753,0.00009465135,0.00007729934,0.000002712208,0.00002476536,0.9802716,0.00001127174,0.009514283,0.004947431,0.0004452122],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01348791,0.00002213046,0.9758828,0.0002946609,0.0005104148,0.0002677724,0.00002687785,0.0002936709,0.009213706],"genre_scores_gemma":[0.9734902,0.00005300426,0.02295982,0.00006619619,0.00009042518,7.232122e-7,0.0001530434,0.00001438958,0.003172164],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9600023,"threshold_uncertainty_score":0.9999551,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1013317018962763,"score_gpt":0.2065412029415,"score_spread":0.1052095010452237,"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."}}