{"id":"W2952043232","doi":"10.48550/arxiv.1403.7135","title":"On subgradient projectors","year":2014,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Optimization and Variational Analysis","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Subgradient method; Mathematics; Computer science; Mathematical optimization","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":[0.0001621211,0.000196657,0.000208952,0.0003395316,0.0001172678,0.0001117193,0.001104705,0.000144775,0.00006858823],"category_scores_gemma":[0.00003820946,0.0002040491,0.0002018739,0.0005781364,0.00003011896,0.0001322893,0.000665058,0.0002585614,0.0002189224],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001478863,"about_ca_system_score_gemma":0.0001105938,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000736343,"about_ca_topic_score_gemma":0.00001614062,"domain_scores_codex":[0.9986693,0.0001175757,0.0001337291,0.0007920198,0.0001029491,0.000184441],"domain_scores_gemma":[0.9986483,0.00008705739,0.0001837414,0.0008214071,0.0001451485,0.0001143469],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003479378,0.00003924596,0.0003114864,0.000006920742,0.00003480026,0.000009602774,0.00004026696,0.4678722,4.279166e-7,0.5312835,0.0003467655,0.00005130244],"study_design_scores_gemma":[0.0001723201,0.00003349364,0.0007803804,0.00002042946,0.00003517062,4.199507e-7,0.000003886058,0.9375249,0.00001224092,0.06050261,0.0006804228,0.0002337307],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02985942,0.00000450522,0.9631956,0.0002632692,0.0004023348,0.0001352425,0.000005159553,0.0001887863,0.005945655],"genre_scores_gemma":[0.9948865,0.00001994158,0.001950035,0.0002969445,0.00004887513,6.740401e-7,0.00002466398,0.000008131982,0.002764295],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.965027,"threshold_uncertainty_score":0.8320881,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05283505477446836,"score_gpt":0.1749155112617012,"score_spread":0.1220804564872329,"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."}}