{"id":"W2625071797","doi":"","title":"Adjusting for Selection Bias Using Gaussian Process Models","year":2014,"lang":"en","type":"article","venue":"TSpace (University of Toronto)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gaussian process; Selection (genetic algorithm); Process (computing); Computer science; Gaussian; Econometrics; Artificial intelligence; Statistics; Machine learning; Data mining; Mathematics; Physics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.03237285,0.001574288,0.002566653,0.003469198,0.001387117,0.002942512,0.004171871,0.002133847,0.005392723],"category_scores_gemma":[0.1152901,0.001046804,0.003581403,0.003306461,0.002492667,0.003966184,0.003558843,0.004250985,0.001474988],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00173422,"about_ca_system_score_gemma":0.003338212,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007556097,"about_ca_topic_score_gemma":0.006135864,"domain_scores_codex":[0.9801123,0.01291587,0.0007080644,0.002511307,0.003131865,0.000620547],"domain_scores_gemma":[0.9318628,0.05441518,0.004235676,0.005560539,0.003501975,0.0004239386],"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":[0.0001301343,0.0001476361,0.01433231,0.0004036244,0.001182804,0.000346898,0.001023399,0.1933074,0.001322154,0.5000215,0.006093007,0.2816892],"study_design_scores_gemma":[0.00007185229,0.0001165072,0.003056345,0.0001950406,0.00027338,0.000190201,0.0001463315,0.5908471,0.001292581,0.3894646,0.01423218,0.0001138618],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001895786,0.0002204878,0.9967695,0.0002092773,0.00006435331,0.0000464091,0.00003492962,0.0001455867,0.0006135822],"genre_scores_gemma":[0.1537122,0.002089625,0.8357591,0.0008768729,0.0004948642,0.0006471821,0.000503285,0.0003821269,0.005534713],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03237285,"threshold_uncertainty_score":0.171206,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04407215040584895,"score_gpt":0.2635499700571142,"score_spread":0.2194778196512653,"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."}}