{"id":"W2147876569","doi":"10.1109/tkde.2015.2453171","title":"RankRC: Large-Scale Nonlinear Rare Class Ranking","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Knowledge and Data Engineering","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Robustness (evolution); Kernel (algebra); Machine learning; Artificial intelligence; Focus (optics); Class (philosophy); Nonlinear system; Rare events; Algorithm; Computational complexity theory; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000444063,0.0001707125,0.0001716955,0.0001872728,0.0001549248,0.0001609138,0.0006236871,0.00007345687,0.00001341924],"category_scores_gemma":[0.0000157876,0.0001742929,0.00003781856,0.0003758133,0.00001622063,0.0008655317,0.00002269912,0.0002992652,0.000114158],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003378848,"about_ca_system_score_gemma":0.00006294948,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004226001,"about_ca_topic_score_gemma":0.00002262959,"domain_scores_codex":[0.9988549,0.00003637177,0.0002022812,0.0004402709,0.0001787096,0.000287424],"domain_scores_gemma":[0.9988402,0.0001032166,0.000029809,0.000737384,0.00006705767,0.0002223567],"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":[0.0001451498,0.001211941,0.0001110533,0.0003692108,0.0003146657,0.0001165883,0.0265561,0.3652972,0.003536103,0.004912726,0.00704809,0.5903811],"study_design_scores_gemma":[0.0008738352,0.00003684247,0.00002467952,0.00004337064,0.00001229662,0.00002069666,0.0001335672,0.8721533,0.0006308228,0.00001290072,0.1258609,0.000196752],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00131792,0.0004720717,0.9955508,0.0001210872,0.0009526185,0.00009991909,0.00004655867,0.0003447604,0.00109427],"genre_scores_gemma":[0.8816363,0.0001073398,0.1170084,0.0001474939,0.0002103594,0.00001942281,0.00004778829,0.00004183437,0.0007811129],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8803183,"threshold_uncertainty_score":0.7107459,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03825382451170466,"score_gpt":0.2748310372290403,"score_spread":0.2365772127173357,"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."}}