{"id":"W2807177388","doi":"10.3389/fvets.2018.00099","title":"Selection and Misclassification Biases in Longitudinal Studies","year":2018,"lang":"en","type":"article","venue":"Frontiers in Veterinary Science","topic":"Health disparities and outcomes","field":"Social Sciences","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Prince Edward Island; Université de Montréal","funders":"","keywords":"Selection (genetic algorithm); Selection bias; Biology; Computer science; Statistics; Artificial intelligence; Mathematics","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":[0.001522716,0.00005925504,0.0001149344,0.0003524076,0.0005791543,0.00005841484,0.0001645681,0.00003434088,0.000009509738],"category_scores_gemma":[0.0008724476,0.0000578328,0.000009083419,0.001219758,0.001598941,0.0006766255,0.0000469,0.00006654293,0.000002675462],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003358287,"about_ca_system_score_gemma":0.0002849657,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009600388,"about_ca_topic_score_gemma":0.002208663,"domain_scores_codex":[0.998845,0.00009925332,0.0001536855,0.0002630765,0.0002296572,0.0004092893],"domain_scores_gemma":[0.9996531,0.00007576844,0.00004044631,0.00007101199,0.00007219982,0.00008748502],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003267102,0.00002899679,0.9692153,0.00003549831,0.000001537732,0.000006638609,0.01061348,0.000004113133,0.0001452029,0.002124644,0.001037308,0.0167546],"study_design_scores_gemma":[0.0001284943,0.0002009963,0.9643394,0.00009154925,0.000001235571,0.000003590275,0.01404847,0.001199045,0.00002606452,0.000790381,0.01907283,0.00009793115],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9939173,0.001138056,0.0001284772,0.001463979,0.001264703,0.0001325705,6.911313e-7,0.00001993067,0.001934327],"genre_scores_gemma":[0.995371,0.001203574,0.002932166,0.0001704442,0.0001048739,0.00001053786,1.838567e-7,0.00000230822,0.0002049015],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01803553,"threshold_uncertainty_score":0.5891365,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1566926449946397,"score_gpt":0.4293469425836905,"score_spread":0.2726542975890508,"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."}}