{"id":"W2133279024","doi":"10.1002/cjs.10032","title":"Variable selection in spatial regression via penalized least squares","year":2009,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Statistics; Feature selection; Mathematics; Least-squares function approximation; Least absolute deviations; Oracle; Regression analysis; Variable (mathematics); Partial least squares regression; Selection (genetic algorithm); Regression; Linear regression; Ordinary least squares; Generalized least squares; Applied mathematics; Computer science; Estimator; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":true,"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":[0.008927406,0.0007740046,0.0013504,0.001540026,0.0004438886,0.001171779,0.001880279,0.0008318274,0.001240589],"category_scores_gemma":[0.03297236,0.0006583215,0.000855515,0.002463107,0.001618957,0.001148797,0.001670138,0.001448152,0.0003759094],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007048929,"about_ca_system_score_gemma":0.001184892,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003552936,"about_ca_topic_score_gemma":0.002809572,"domain_scores_codex":[0.9891977,0.008813826,0.0002065683,0.0007863074,0.0007899765,0.0002056841],"domain_scores_gemma":[0.9768878,0.01933187,0.001152652,0.0009501077,0.001474905,0.0002026414],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001663911,0.00007810056,0.004324035,0.0001782916,0.0002649637,0.0001632603,0.0001108352,0.847562,0.00156908,0.03954128,0.002084189,0.1039577],"study_design_scores_gemma":[0.00001276916,0.00002041637,0.0002624695,0.000007793121,0.000008823279,0.00001411725,0.000005543595,0.9897286,0.0003146514,0.009188099,0.0004292027,0.000007489705],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006711626,0.0001291525,0.9928222,0.00009226528,0.00001387475,0.00001710058,0.00002138066,0.00009086239,0.0001015303],"genre_scores_gemma":[0.3042059,0.0003774577,0.6928995,0.0001689642,0.0001416679,0.0002769568,0.0003768003,0.0001750899,0.001377662],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008927406,"threshold_uncertainty_score":0.0472132,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00831863964060954,"score_gpt":0.2107379394837311,"score_spread":0.2024192998431216,"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."}}