{"id":"W6925273490","doi":"10.17632/s662r5mkxx","title":"Why Resident Identification Matters","year":2023,"lang":"en","type":"dataset","venue":"Mendeley Data","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Identification (biology); Data collection; Descriptive statistics; Descriptive research; Survey research; Population","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":[0.00742697,0.0001337027,0.0003071577,0.001079285,0.001293137,0.001450766,0.0008724419,0.0009180235,0.01148301],"category_scores_gemma":[0.06226147,0.000278729,0.0003196214,0.003083566,0.0005984973,0.002446543,0.001395613,0.001058689,0.002819689],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001200074,"about_ca_system_score_gemma":0.003085739,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02328186,"about_ca_topic_score_gemma":0.03580689,"domain_scores_codex":[0.9933982,0.002892812,0.0007167529,0.0008096261,0.001164516,0.001017938],"domain_scores_gemma":[0.9718665,0.01203325,0.006396032,0.002030683,0.005284545,0.002388906],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001804363,0.0001225643,0.6764829,0.0007382568,0.00007082167,0.0003143635,0.00570019,0.0001067917,0.0001160219,0.003461437,0.2367243,0.07598196],"study_design_scores_gemma":[0.00004673343,0.0001366136,0.8031706,0.002116336,0.00009124056,0.0009359063,0.02662677,0.000583099,0.0003085804,0.003172769,0.1627654,0.00004595141],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"dataset","genre_scores_codex":[0.6722956,0.007522807,0.003493356,0.2081248,0.002894912,0.0004205029,0.04624665,0.000202442,0.05879899],"genre_scores_gemma":[0.94712,0.004071222,0.001456846,0.02354166,0.001217286,0.0004816931,0.01389854,0.0001038723,0.008108859],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.02328186,"threshold_uncertainty_score":0.04629272,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06171907503118503,"score_gpt":0.317832689814207,"score_spread":0.256113614783022,"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."}}