{"id":"W3092086356","doi":"10.48550/arxiv.2010.03962","title":"Test-Cost Sensitive Methods for Identifying Nearby Points","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Data Stream Mining Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Feature (linguistics); Set (abstract data type); Artificial intelligence; Point (geometry); Machine learning; Data mining; Tree (set theory); Data set; Test set; Reinforcement learning; Test data; Decision tree; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01097923,0.001926369,0.002692947,0.002916076,0.001105488,0.001951054,0.005503822,0.004368098,0.003844914],"category_scores_gemma":[0.05894861,0.001035938,0.0012894,0.002694061,0.003287159,0.004718322,0.00451239,0.005242553,0.0009921158],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002129893,"about_ca_system_score_gemma":0.001817891,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002855387,"about_ca_topic_score_gemma":0.002728406,"domain_scores_codex":[0.9939663,0.002751768,0.0003544834,0.001111842,0.001555256,0.000260171],"domain_scores_gemma":[0.9463516,0.04083265,0.004116789,0.004756635,0.002995455,0.000946908],"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":[0.0005916454,0.0004185379,0.009705764,0.0003802505,0.0003102906,0.0003249592,0.0002787189,0.7029191,0.003138098,0.05156709,0.006247236,0.2241184],"study_design_scores_gemma":[0.00003075503,0.00008135919,0.0006565714,0.00003036254,0.00002114218,0.000106069,0.00002856556,0.966558,0.001002181,0.03064177,0.0008206884,0.00002248574],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01669085,0.0008531443,0.979815,0.0006745324,0.00007804526,0.0001385804,0.0001137713,0.0005109675,0.001125085],"genre_scores_gemma":[0.5492675,0.000693911,0.4428293,0.0008447466,0.0003741897,0.0005398868,0.0006048441,0.0003789531,0.004466709],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01097923,"threshold_uncertainty_score":0.05806446,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2006222045870076,"score_gpt":0.3003322735108832,"score_spread":0.09971006892387557,"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."}}