{"id":"W2121824931","doi":"","title":"The P-Norm Push: A Simple Convex Ranking Algorithm that Concentrates at the Top of the List","year":2009,"lang":"en","type":"article","venue":"DSpace@MIT (Massachusetts Institute of Technology)","topic":"Optimization and Search Problems","field":"Computer Science","cited_by":143,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"York University; National Science Foundation","keywords":"Ranking (information retrieval); Generalization; Computer science; Norm (philosophy); Ranking SVM; Learning to rank; Regular polygon; Boosting (machine learning); Simple (philosophy); Algorithm; Mathematics; Machine learning","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005503947,0.001675863,0.002404847,0.001669153,0.00099351,0.002444292,0.002058404,0.002486665,0.004430805],"category_scores_gemma":[0.01107466,0.0006839223,0.0009954832,0.001785785,0.002065089,0.004337071,0.002512634,0.00223392,0.002804329],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009491493,"about_ca_system_score_gemma":0.001751938,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008973855,"about_ca_topic_score_gemma":0.001199538,"domain_scores_codex":[0.9979019,0.0008584376,0.0001221184,0.0003546698,0.0005926719,0.0001701275],"domain_scores_gemma":[0.996094,0.001752803,0.0004180447,0.0006852529,0.0007986733,0.0002511062],"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":[0.0004990648,0.0003453497,0.00191799,0.0005780004,0.000179437,0.0001568539,0.0002241954,0.2884561,0.01276591,0.08974797,0.02812102,0.5770081],"study_design_scores_gemma":[0.00007192719,0.0004154274,0.0004804762,0.00004828356,0.00003634166,0.0001963523,0.00005969757,0.9114503,0.008460974,0.07040114,0.008322808,0.00005623577],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00936603,0.0002284593,0.9861605,0.0004146487,0.00006562281,0.00009848746,0.00008760457,0.0006028924,0.002975714],"genre_scores_gemma":[0.1792045,0.0004008778,0.8094571,0.0006280435,0.0002502097,0.0004332196,0.0005046878,0.0006523607,0.008469056],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005503947,"threshold_uncertainty_score":0.02910805,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0132587340757605,"score_gpt":0.2457731972886478,"score_spread":0.2325144632128873,"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."}}