{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002358596,0.001566963,0.001573631,0.001411127,0.0007190891,0.001435517,0.001708978,0.00268909,0.009005186],"category_scores_gemma":[0.008369286,0.0006346091,0.001418406,0.001140967,0.001350063,0.002072152,0.002190821,0.002019888,0.002561493],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007553117,"about_ca_system_score_gemma":0.001034546,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001521393,"about_ca_topic_score_gemma":0.001832012,"domain_scores_codex":[0.9988759,0.0003846305,0.00007901847,0.0002258812,0.0003403039,0.00009419324],"domain_scores_gemma":[0.99733,0.001721238,0.0001673354,0.0002457127,0.0004752294,0.00006060371],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001267035,0.0001008648,0.0009164418,0.0003325577,0.00009892738,0.0002875522,0.0002101473,0.6198387,0.004875117,0.1429737,0.01164331,0.2185959],"study_design_scores_gemma":[0.00001674056,0.0000482935,0.00009404148,0.00002876296,0.0000113858,0.0001132191,0.00002052043,0.9456753,0.000908152,0.04784422,0.005229682,0.000009636957],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.001935815,0.0002180762,0.9931828,0.0002669797,0.00002939142,0.00004812075,0.00006030758,0.0001525183,0.00410592],"genre_scores_gemma":[0.1819747,0.0008991573,0.797668,0.0006959606,0.0002375166,0.0005296645,0.000621176,0.0003630361,0.01701075],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.009005186,"threshold_uncertainty_score":0,"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."}}