{"id":"W2431848029","doi":"","title":"Rural medicine and rural training: addressing high-technology care.","year":2008,"lang":"fr","type":"article","venue":"PubMed","topic":"Global Health Workforce Issues","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"NOSM University","funders":"","keywords":"Training (meteorology); Medical education; Medicine; Geography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00295778,0.0002224248,0.0002740815,0.001059982,0.002023125,0.00261639,0.001010893,0.006303773,0.04259437],"category_scores_gemma":[0.01094119,0.0001306528,0.0002364943,0.001455953,0.002066943,0.004053785,0.002924497,0.003184169,0.002211597],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002187927,"about_ca_system_score_gemma":0.02079545,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0151119,"about_ca_topic_score_gemma":0.03355453,"domain_scores_codex":[0.9983102,0.0006596781,0.00009425166,0.00008368482,0.0003259573,0.000526163],"domain_scores_gemma":[0.9882498,0.00453231,0.0009927861,0.0002110058,0.001593034,0.004421104],"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":[0.00006003797,0.0001889106,0.008995897,0.002956936,0.00001750597,0.0006951116,0.003069954,0.0001371043,0.0007956664,0.03083576,0.6588458,0.2934014],"study_design_scores_gemma":[0.00004444956,0.0001627191,0.02978997,0.004145297,0.00001735193,0.0009527823,0.01325899,0.0001238568,0.0001613089,0.01175056,0.9395661,0.00002651517],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.005475258,0.07222558,0.0002792717,0.8904991,0.00602807,0.00002172029,0.0001159274,0.00004663253,0.02530853],"genre_scores_gemma":[0.2395316,0.2816194,0.002875934,0.374208,0.03718846,0.0001656511,0.0005718454,0.00006481959,0.06377425],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04259437,"threshold_uncertainty_score":0.1424924,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1407469194615193,"score_gpt":0.3894535157973433,"score_spread":0.248706596335824,"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."}}