{"id":"W3008560015","doi":"10.1109/icmla.2019.00197","title":"Ranking Clusters of Postal Codes to Improve Recruitment in the Canadian Armed Forces","year":2019,"lang":"en","type":"article","venue":"","topic":"Census and Population Estimation","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Department of National Defence","funders":"","keywords":"Ranking (information retrieval); Rank (graph theory); Set (abstract data type); Unsupervised learning; Computer science; Population; Military personnel; Machine learning; Training set; Artificial intelligence; Operations research; Engineering; Mathematics; Political science; Medicine; Law; Combinatorics","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":[0.002270461,0.0005561835,0.0006115469,0.003369897,0.001766432,0.001467041,0.001057343,0.0003125783,0.00318976],"category_scores_gemma":[0.01133914,0.0001698379,0.0003817602,0.002914389,0.0004325795,0.000444215,0.001038753,0.0006064799,0.0007595051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004722603,"about_ca_system_score_gemma":0.01405006,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6115223,"about_ca_topic_score_gemma":0.7970158,"domain_scores_codex":[0.9986093,0.0003268747,0.00006230165,0.0001818303,0.000458963,0.000360747],"domain_scores_gemma":[0.9963671,0.0006909057,0.0003603421,0.0002367935,0.001955523,0.0003892912],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005044713,0.0004601568,0.2564138,0.0002101223,0.0001546969,0.00008505763,0.001627699,0.03658486,0.003869054,0.005442324,0.03951089,0.6551369],"study_design_scores_gemma":[0.0001217609,0.0003864822,0.5298542,0.0001895328,0.0001463761,0.00009294708,0.005628667,0.4162624,0.007299324,0.008003592,0.03184385,0.0001707505],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8996539,0.0007651634,0.07577748,0.001453815,0.0001784926,0.0007605074,0.004943683,0.001405249,0.01506185],"genre_scores_gemma":[0.9211822,0.0002119154,0.06707193,0.0001096733,0.00003179657,0.0002070674,0.004724001,0.0001170924,0.00634431],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3884777,"threshold_uncertainty_score":0.7815311,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08154883476620853,"score_gpt":0.3465379927603617,"score_spread":0.2649891579941531,"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."}}