{"id":"W4394560312","doi":"10.6084/m9.figshare.21152743","title":"NATO allies’ armed force personnel as a share of total labor force, total labor force, military expenditure as a share of GDP, and GDP, 1991–2019","year":2022,"lang":"en","type":"dataset","venue":"Figshare","topic":"Defense, Military, and Policy Studies","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Economics; Labour economics","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":[0.0006345743,0.0007884082,0.0007018308,0.003250873,0.0004188079,0.001146967,0.001041527,0.0005435321,0.02045231],"category_scores_gemma":[0.004010244,0.0004264229,0.0007587738,0.007904422,0.0001433147,0.0008811014,0.001190009,0.001051528,0.01504115],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001814504,"about_ca_system_score_gemma":0.00262234,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.132066,"about_ca_topic_score_gemma":0.1299271,"domain_scores_codex":[0.99918,0.00008492247,0.0001700419,0.0001534435,0.0002470396,0.0001644645],"domain_scores_gemma":[0.9980489,0.0001926352,0.000605609,0.0001025201,0.0008647121,0.0001856221],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001218053,0.0000319559,0.02360974,0.0009398756,0.00009217789,0.00004663203,0.00009753899,0.0003639618,0.00005686643,0.0007609581,0.9680673,0.005811198],"study_design_scores_gemma":[0.0001701624,0.00003829285,0.2544739,0.001113909,0.000110504,0.0001213331,0.0007487558,0.0003862325,0.0002539356,0.0003358756,0.7422053,0.00004174446],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001780985,0.0001936541,0.00003304347,0.0001179036,0.00004133709,0.00001949457,0.9960297,0.00002452881,0.001759401],"genre_scores_gemma":[0.004704721,0.0003616373,0.000161826,0.00009912217,0.00003256658,0.0001932155,0.9915046,0.00001973995,0.002922509],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.132066,"threshold_uncertainty_score":0.2625945,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02585944314821552,"score_gpt":0.2471126486428609,"score_spread":0.2212532054946454,"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."}}