{"id":"W4300995395","doi":"","title":"FSU immigrants in Canada: a case of positive triple selection?","year":2009,"lang":"en","type":"preprint","venue":"ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam)","topic":"Migration and Labor Dynamics","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Immigration; Selection (genetic algorithm); Political science; Demographic economics; Computer science; Economics; Artificial intelligence; Law","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"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":[0.001228191,0.0002976981,0.0005650013,0.00133621,0.01170618,0.002190913,0.001063589,0.001145995,0.00310628],"category_scores_gemma":[0.004058191,0.0001757701,0.0002725456,0.003768506,0.00378747,0.0005664212,0.001606669,0.001105389,0.0001615563],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02025999,"about_ca_system_score_gemma":0.04586085,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9856644,"about_ca_topic_score_gemma":0.992592,"domain_scores_codex":[0.9986305,0.0001430412,0.00002252491,0.0001178443,0.0002560874,0.0008300254],"domain_scores_gemma":[0.9979705,0.0002953957,0.0003752136,0.0001621009,0.0005667378,0.0006299258],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006341193,0.0002436919,0.9051186,0.00004981654,0.00006176705,0.006005656,0.02401735,0.0003706942,0.0007624966,0.01510743,0.004908684,0.04271968],"study_design_scores_gemma":[0.00013733,0.0001969253,0.865078,0.0001327547,0.0001069719,0.003154686,0.1069625,0.002340376,0.000820062,0.004145811,0.0168203,0.0001042988],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.991735,0.000277944,0.0001015494,0.001644089,0.00001432962,0.00003090134,0.0001674075,0.000005954717,0.006022977],"genre_scores_gemma":[0.9980926,0.0002327212,0.0001584418,0.000324318,0.000007415279,0.0000101946,0.00007260812,0.000003960182,0.00109775],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02025999,"threshold_uncertainty_score":0.1469972,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01215495443517907,"score_gpt":0.2737671060769606,"score_spread":0.2616121516417815,"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."}}