{"id":"W4310896317","doi":"10.1016/j.heliyon.2022.e12181","title":"Knowledge mapping of population health: A bibliometric analysis","year":2022,"lang":"en","type":"article","venue":"Heliyon","topic":"Health disparities and outcomes","field":"Social Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"China Postdoctoral Science Foundation","keywords":"Population; Bibliometrics; Population health; Status quo; Political science; Geography; Social science; Library science; Medicine; Sociology; Environmental health; Computer science; 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":"codex-gemma-dda1882f352a","candidate_categories":["bibliometrics"],"consensus_categories":["bibliometrics"],"category_scores_codex":[0.001525236,0.00004275289,0.0002079648,0.02630668,0.000787007,0.00001753503,0.0001279473,0.00002340915,0.0008511248],"category_scores_gemma":[0.0001659195,0.00004693014,0.0001074483,0.1418009,0.00002143193,0.00006896761,0.00005181315,0.00008229548,0.00000745969],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002667608,"about_ca_system_score_gemma":0.0001815043,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02591672,"about_ca_topic_score_gemma":0.007236737,"domain_scores_codex":[0.9985799,0.0003708763,0.0002821348,0.0001189221,0.0003698379,0.0002783817],"domain_scores_gemma":[0.999414,0.0001703592,0.0001480111,0.0001079428,0.00004964681,0.0001100485],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000004541236,0.0000790404,0.9457652,0.0004197671,0.000048457,4.94404e-7,0.01107295,0.0001589583,0.00000121888,0.01244803,0.001147105,0.02885422],"study_design_scores_gemma":[0.00006730271,0.00001844791,0.8429234,0.00001516401,0.00001419829,7.196054e-8,0.004344375,0.00006259874,5.559215e-7,0.00008322469,0.1524249,0.00004579225],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9596986,0.02106949,0.0002785789,0.008004044,0.0005763369,0.0003067954,0.00003003146,0.00007484586,0.009961264],"genre_scores_gemma":[0.9962218,0.001557112,0.0001396394,0.0009720963,0.00007320951,0.00001760438,0.00002144468,0.000004201869,0.0009928913],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1512778,"threshold_uncertainty_score":0.9847293,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08690656072275363,"score_gpt":0.4037187720645823,"score_spread":0.3168122113418287,"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."}}