{"id":"W4285723483","doi":"10.7249/rra647-1","title":"Assessing Health and Human Services Needs to Support an Integrated &lt;em&gt;Health in All Policies&lt;/em&gt; Plan for Prince George's County, Maryland","year":2020,"lang":"en","type":"book","venue":"RAND Corporation eBooks","topic":"Public Health Policies and Education","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"George (robot); Plan (archaeology); Health plan; Human services; Political science; Library science; Business; Geography; Computer science; Health care; Archaeology; Law; Artificial intelligence","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":[0.000953271,0.0003694297,0.0001551972,0.00111484,0.0008047218,0.00242257,0.0006831214,0.0008625499,0.04123791],"category_scores_gemma":[0.002116308,0.0003214669,0.0001831251,0.0005967265,0.0002708539,0.001029253,0.0006934223,0.0007136039,0.008831572],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002731286,"about_ca_system_score_gemma":0.007461565,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0965578,"about_ca_topic_score_gemma":0.3781578,"domain_scores_codex":[0.9997095,0.00006792389,0.00001082667,0.00001982872,0.0001442766,0.00004747195],"domain_scores_gemma":[0.9990351,0.0003616324,0.00004281012,0.00002559026,0.0003165681,0.0002183583],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.00002471733,0.00008019886,0.003816935,0.00007125107,0.000003036521,0.00003437938,0.0001420545,0.0005317216,0.0001423551,0.003039256,0.8574276,0.1346864],"study_design_scores_gemma":[0.00006148364,0.0001605043,0.05548229,0.0007884013,0.00003121216,0.0001991208,0.003163371,0.003010236,0.0009455783,0.006926677,0.9291965,0.00003467686],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.01826417,0.004304024,0.002094641,0.02806669,0.001014818,0.0002841265,0.007761294,0.0008902777,0.9373201],"genre_scores_gemma":[0.1240668,0.007248817,0.01864329,0.004984006,0.0004109533,0.0003876265,0.008316178,0.0002013814,0.835741],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0965578,"threshold_uncertainty_score":0.1919915,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1051934178817465,"score_gpt":0.4214059235903164,"score_spread":0.3162125057085699,"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."}}