{"id":"W4413783546","doi":"10.23889/ijpds.v10i4.3284","title":"A study of occupational employment and retention using linked occupational licensing, education and population registry data","year":2025,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Educational Robotics and Engineering","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Business; Population; Environmental health; Medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.02055815,0.0002294815,0.0003385733,0.003895566,0.003203684,0.002494079,0.001437725,0.0005212079,0.001722612],"category_scores_gemma":[0.05208505,0.000375635,0.0004975145,0.008555402,0.001321752,0.001960694,0.004207331,0.0009366847,0.000319936],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00513145,"about_ca_system_score_gemma":0.01398468,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2314395,"about_ca_topic_score_gemma":0.2807893,"domain_scores_codex":[0.9795024,0.0101697,0.00170247,0.001365868,0.005115254,0.002144362],"domain_scores_gemma":[0.9590423,0.01523163,0.01225395,0.003889983,0.008138687,0.001443334],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003684839,0.0001508588,0.9759278,0.00006491588,0.0000551244,0.00006711265,0.008589359,0.000129159,0.00007427469,0.0005710953,0.0004135632,0.01391985],"study_design_scores_gemma":[0.00001873799,0.0002559846,0.9429325,0.0003254907,0.0000594506,0.0001630786,0.0480935,0.001508797,0.0003130863,0.0004732024,0.005818314,0.00003788046],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9948657,0.0002368343,0.001325668,0.0003983103,0.00001048636,0.0001809493,0.0008335453,0.00000861066,0.002139933],"genre_scores_gemma":[0.9959981,0.0002094928,0.00171571,0.0001057377,0.00001050223,0.0003086451,0.0008403284,0.000007981859,0.0008035089],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2314395,"threshold_uncertainty_score":0.4601848,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1726670310036161,"score_gpt":0.4547795320056041,"score_spread":0.282112501001988,"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."}}