{"id":"W2170865247","doi":"10.1145/2487575.2487671","title":"FeaFiner","year":2013,"lang":"en","type":"article","venue":"","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Orthogonality; Feature (linguistics); Feature selection; Interpretability; Generalization; Computer science; Smoothness; Convexity; Augmented Lagrangian method; Consistency (knowledge bases); Mathematical optimization; Process (computing); Artificial intelligence; Mathematics; Algorithm","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000004641364,0.00003021847,0.00002727918,0.00001378907,0.000005998932,0.00001329899,0.00003070951,0.00001451445,0.0006930461],"category_scores_gemma":[0.000001019129,0.0000241595,0.000009813518,0.00002314863,0.000003997978,0.00004049521,0.000006079304,0.00002316142,0.0005834765],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003107385,"about_ca_system_score_gemma":4.785906e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002788158,"about_ca_topic_score_gemma":0.000001026918,"domain_scores_codex":[0.9998633,0.000001089463,0.00002902843,0.00002701789,0.0000215384,0.00005797749],"domain_scores_gemma":[0.999892,0.000003729906,0.000001379676,0.00007819887,0.00000981219,0.00001488089],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[1.248176e-7,0.000003982123,0.0002089141,0.000001920246,0.000009763657,0.000001422507,0.00001838202,0.0006109492,0.08162761,0.002507501,0.8773712,0.03763826],"study_design_scores_gemma":[0.0001246345,0.00001946434,0.01012392,0.00001847896,0.00000480495,0.00001216721,0.0000295196,0.1891581,0.6375169,0.01388631,0.1487407,0.0003650043],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2765516,0.00007674001,0.02512177,0.0001201753,0.0001263499,0.00007994785,1.080632e-7,0.00283121,0.6950921],"genre_scores_gemma":[0.9938022,0.000006211076,0.005012455,0.0001058826,0.00002610399,0.000005421051,3.047804e-7,0.000007054624,0.001034288],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7286304,"threshold_uncertainty_score":0.7588369,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004797072840860334,"score_gpt":0.1577284605772174,"score_spread":0.1529313877363571,"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."}}