{"id":"W3166867317","doi":"10.1097/01.ccn.0000503421.75099.56","title":"Deciding if and when to retire","year":2016,"lang":"en","type":"article","venue":"Nursing Critical Care","topic":"Retirement, Disability, and Employment","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Smiths Detection (Canada)","funders":"","keywords":"Computer science; Business","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":[0.0002457528,0.00007653789,0.000102588,0.00002639121,0.0004428378,0.00009175214,0.0001120237,0.00007104508,0.0001724253],"category_scores_gemma":[0.001508361,0.00005895673,0.00003517266,0.00006449532,0.0005871911,0.0001223526,0.00002422614,0.0000442366,0.00003609687],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003514407,"about_ca_system_score_gemma":0.00004525834,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001513176,"about_ca_topic_score_gemma":0.0001806172,"domain_scores_codex":[0.9987977,0.0001159521,0.0001145035,0.0002634146,0.0003364651,0.0003720096],"domain_scores_gemma":[0.9991423,0.0002592168,0.000007413982,0.0001412146,0.0001298829,0.0003199032],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00004891368,0.0001099772,0.09055358,0.0001104489,0.000005851439,0.000008119226,0.1555055,1.763145e-7,0.001222147,0.2638353,0.0124223,0.4761776],"study_design_scores_gemma":[0.001345151,0.001010046,0.09729661,0.004592331,0.0001919262,0.000005473837,0.3477654,0.000008284903,0.003789653,0.3533364,0.1890873,0.001571383],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7340475,0.0006269951,0.001390462,0.1093835,0.001303838,0.0003792304,0.00001608368,0.0001689913,0.1526834],"genre_scores_gemma":[0.9984154,0.00001101837,0.0006582725,0.0003520382,0.0002840936,0.000008877497,5.193367e-7,0.000008243359,0.0002615375],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4746063,"threshold_uncertainty_score":0.3405995,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2210172885956405,"score_gpt":0.486413440609015,"score_spread":0.2653961520133744,"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."}}