{"id":"W6958067863","doi":"10.6068/dp14ba844c6e581","title":"Most Recent Data (2012). Statistics Canada. CANSIM: Health - Health Care Services | Country: Canada | Table: Neurological conditions in institutions, by age, sex, and number of residents | Variable: 0 to 17 years, All conditions, Both sexes, 10 to 49 residents | Units: #, 2012. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-112.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Health care; Health statistics; Census; Socioeconomic status; Official statistics; Medical statistics; Economic statistics; Medical prescription; Health policy","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":[0.002767622,0.002559221,0.003150216,0.008197751,0.003705335,0.005277047,0.005625694,0.001886244,0.1429052],"category_scores_gemma":[0.02758153,0.00190361,0.002627041,0.04325915,0.0007140744,0.002501961,0.002679378,0.00356594,0.06728402],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.06292507,"about_ca_system_score_gemma":0.1533216,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9945548,"about_ca_topic_score_gemma":0.9926507,"domain_scores_codex":[0.9948236,0.0003608512,0.0006588912,0.000595939,0.002398842,0.001161863],"domain_scores_gemma":[0.9519931,0.002397783,0.001335047,0.001277374,0.04048449,0.002512287],"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":[0.00001891557,0.000006032994,0.0006174885,0.0002639471,0.00001517581,0.000004323057,0.00001560009,0.00007927691,0.000005418167,0.0002153655,0.997584,0.001174606],"study_design_scores_gemma":[0.0002766847,0.00001552707,0.02478386,0.001441738,0.00009185277,0.00002864039,0.0005176659,0.0004722878,0.0001388208,0.0007812169,0.9713382,0.0001133453],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00003175853,0.00004493631,0.00001476221,0.0001470503,0.00002412718,0.00001527527,0.9988793,0.00004312084,0.0007997109],"genre_scores_gemma":[0.0008396342,0.0003324143,0.0004107651,0.0002922187,0.00002546698,0.0001831885,0.9938669,0.0001183009,0.003931162],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1429052,"threshold_uncertainty_score":0.4780656,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03723743757599414,"score_gpt":0.316049660669955,"score_spread":0.2788122230939609,"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."}}