{"id":"W2802650647","doi":"10.1097/01.naj.0000532813.12208.b8","title":"Saving the Safety Net","year":2018,"lang":"en","type":"letter","venue":"AJN American Journal of Nursing","topic":"Healthcare Policy and Management","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Thompson Rivers University","funders":"","keywords":"Safety net; Privilege (computing); Legislation; Government (linguistics); Health care; Business; State (computer science); Economic growth; Medicine; Political science; Environmental health; Economics; Law; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001140875,0.0002161339,0.0007435409,0.0003674846,0.0002053927,0.0001052552,0.0006212642,0.0001310327,0.0001630825],"category_scores_gemma":[0.0001276612,0.0001821609,0.0002790519,0.0003160935,0.0004553683,0.0001306518,0.00003805666,0.001402667,0.0001585756],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004922653,"about_ca_system_score_gemma":0.00007524983,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005214717,"about_ca_topic_score_gemma":0.000006087629,"domain_scores_codex":[0.9979147,0.0001050899,0.001148175,0.0002338039,0.0001041128,0.0004941073],"domain_scores_gemma":[0.9968039,0.0001729611,0.00245947,0.0004000341,0.00007773053,0.00008590168],"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":[0.00001659368,0.00001709183,0.00007632907,0.00001626666,0.0001085199,0.00004485578,0.001133582,0.00000738146,1.841126e-7,0.002637436,0.8940657,0.101876],"study_design_scores_gemma":[0.0001267086,0.0003045891,0.0009596131,0.0003029124,0.00002956509,0.0001226181,0.0003137551,0.00003247861,5.06389e-7,0.006931025,0.9906791,0.000197056],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.0004040421,0.001578761,0.003571584,0.9679147,0.002329056,0.0001180323,0.00004432371,0.0000109748,0.0240285],"genre_scores_gemma":[0.06009252,0.001070673,0.002014194,0.916774,0.01855441,0.000002909863,0.00001189319,0.00009184703,0.001387606],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.101679,"threshold_uncertainty_score":0.7428305,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06133121483273869,"score_gpt":0.3026208432247907,"score_spread":0.241289628392052,"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."}}