{"id":"W2539573213","doi":"10.1109/tic-sth.2009.5444424","title":"ICE-Lasso: An enhanced form of Lasso selection","year":2009,"lang":"en","type":"article","venue":"","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Lasso (programming language); Selection (genetic algorithm); Computer science; Elastic net regularization; Gesture; Artificial intelligence; Machine learning; Algorithm; Feature selection; World Wide Web","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":[0.002052062,0.001347763,0.001796437,0.001134436,0.001098237,0.001230832,0.002493863,0.001470937,0.0124806],"category_scores_gemma":[0.007965077,0.0006078036,0.0009087074,0.001347187,0.000881912,0.002110985,0.003137358,0.001706172,0.003769919],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003138701,"about_ca_system_score_gemma":0.0008217557,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002080154,"about_ca_topic_score_gemma":0.004912433,"domain_scores_codex":[0.9979091,0.0004898017,0.0001227444,0.0004160729,0.000834721,0.0002274361],"domain_scores_gemma":[0.9958578,0.001811456,0.0002798196,0.001120631,0.0006726422,0.0002576787],"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":[0.002648662,0.0003959725,0.004120443,0.0004006138,0.0002341268,0.0007487624,0.0006800491,0.06430065,0.06015871,0.01351093,0.04628435,0.8065167],"study_design_scores_gemma":[0.0002254641,0.000303304,0.002343294,0.00003942973,0.00006227101,0.0006193225,0.0002008466,0.9100528,0.02766737,0.01695266,0.0413846,0.0001486886],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009025924,0.0001313273,0.9795769,0.0001547344,0.00009465805,0.0001604447,0.000335945,0.006579186,0.003940743],"genre_scores_gemma":[0.2060956,0.0002204746,0.7713754,0.0006101134,0.0003086401,0.0006060492,0.002119828,0.002130573,0.01653339],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0124806,"threshold_uncertainty_score":0.0417518,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008986692784603796,"score_gpt":0.2397956264158289,"score_spread":0.2308089336312251,"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."}}