{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","open_science","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008571345,0.0003263465,0.0003599687,0.0002618616,0.000194441,0.000313884,0.00552302,0.0001447084,0.00003888956],"category_scores_gemma":[0.000162876,0.0003117799,0.00004554628,0.0004929487,0.000053053,0.001879766,0.005438452,0.0003106689,0.006418958],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000765821,"about_ca_system_score_gemma":0.0001144718,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003147555,"about_ca_topic_score_gemma":0.001848355,"domain_scores_codex":[0.9967122,0.0001394307,0.000626339,0.001406083,0.0007076273,0.0004082945],"domain_scores_gemma":[0.9896324,0.0001165755,0.0004156339,0.009649014,0.00006870216,0.0001176655],"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":[0.000001903851,0.0000134864,2.168319e-7,0.0001442035,0.00003385033,0.00007014628,0.00001254485,0.000005390423,0.00002110087,0.0008601449,0.9983576,0.0004793918],"study_design_scores_gemma":[0.00009215988,0.00001140685,0.00001049412,0.0001206661,0.0000223089,0.00001797419,0.00002507718,0.0005261229,0.00002221807,0.0001606024,0.9986535,0.0003374165],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[1.052646e-7,0.000115498,0.223612,0.002610423,0.002017724,0.0002289652,0.7711885,0.0002211323,0.000005676879],"genre_scores_gemma":[6.662081e-8,0.000482694,0.006654331,0.002054807,0.0002948031,0.00005818666,0.9897594,0.00002535687,0.0006703095],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.218571,"threshold_uncertainty_score":0.9999334,"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."}}