{"id":"W4399572903","doi":"10.32614/cran.package.onmarg","title":"onmaRg: Import Public Health Ontario's Ontario Marginalization Index","year":2022,"lang":"en","type":"dataset","venue":"","topic":"Primary Care and Health Outcomes","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Index (typography); Public health; Geography; Political science; Medicine; Computer science; World Wide Web; Nursing","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":[0.002797045,0.00102765,0.001047948,0.00657903,0.003617013,0.004956614,0.002482611,0.0006973788,0.1006085],"category_scores_gemma":[0.01556017,0.0008626279,0.001323103,0.01623403,0.0008462847,0.002348294,0.003628787,0.001196184,0.02711476],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.06221866,"about_ca_system_score_gemma":0.1222517,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9869653,"about_ca_topic_score_gemma":0.9880728,"domain_scores_codex":[0.9950616,0.0002468905,0.000295407,0.000374807,0.002993663,0.001027775],"domain_scores_gemma":[0.9808834,0.0007620212,0.0006771923,0.0009858795,0.0145321,0.002159411],"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":[0.00004036591,0.00001284375,0.008905248,0.0002846318,0.00002153674,0.00002558585,0.0003518807,0.0001442359,0.00005406204,0.003158651,0.9658176,0.02118335],"study_design_scores_gemma":[0.00003015303,0.000008728361,0.04359501,0.0002374905,0.00002704958,0.00002411748,0.0007249999,0.0004035365,0.0001400301,0.0006602471,0.9541071,0.00004158615],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00475901,0.0009544272,0.001219453,0.005593286,0.0004530663,0.0005282253,0.8056346,0.001729195,0.1791287],"genre_scores_gemma":[0.05451389,0.003429995,0.008407841,0.002445066,0.0003245582,0.001571202,0.7197081,0.002132475,0.2074669],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1006085,"threshold_uncertainty_score":0.4514301,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1106754562237953,"score_gpt":0.4161756625648247,"score_spread":0.3055002063410294,"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."}}