{"id":"W2897520795","doi":"10.1007/s10707-018-0332-7","title":"Diverse nearest neighbors queries using linear skylines","year":2018,"lang":"en","type":"article","venue":"GeoInformatica","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Closeness; k-nearest neighbors algorithm; Computer science; Leverage (statistics); Skyline; Set (abstract data type); Euclidean space; Categorical variable; Point (geometry); Space (punctuation); Data mining; Information retrieval; Theoretical computer science; Mathematics; Geography; Combinatorics; Artificial intelligence; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002283988,0.001070116,0.002737045,0.003899035,0.001601074,0.002926596,0.002898934,0.001711843,0.005144739],"category_scores_gemma":[0.01115453,0.0007553851,0.001189683,0.00716054,0.0008483183,0.006720675,0.004339023,0.001242613,0.002210566],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001218747,"about_ca_system_score_gemma":0.001139282,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005668906,"about_ca_topic_score_gemma":0.009973305,"domain_scores_codex":[0.9946817,0.001382267,0.0004546201,0.0009320303,0.002182569,0.0003667867],"domain_scores_gemma":[0.9945016,0.002112015,0.0003792684,0.001878114,0.0008498609,0.0002791439],"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":[0.003404502,0.00112888,0.01142442,0.0007862451,0.0004418857,0.000713365,0.0008164568,0.2701663,0.01824059,0.056159,0.05241216,0.5843062],"study_design_scores_gemma":[0.0001272781,0.0001574803,0.0007131463,0.0000224452,0.00003494729,0.0002847704,0.0002621629,0.9578514,0.004025294,0.03032165,0.006169755,0.0000296115],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08087458,0.001551541,0.8982953,0.0008919997,0.0001878806,0.0004022149,0.004425951,0.007351654,0.006018869],"genre_scores_gemma":[0.566525,0.0006745275,0.415944,0.0002607309,0.0002214676,0.0002584166,0.0116271,0.0004284222,0.004060309],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005668906,"threshold_uncertainty_score":0.0172109,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03201043735417539,"score_gpt":0.2743644551988756,"score_spread":0.2423540178447003,"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."}}