{"id":"W2987228376","doi":"10.1038/s41586-019-1657-6","title":"Sex and gender analysis improves science and engineering","year":2019,"lang":"en","type":"review","venue":"Nature","topic":"Sex and Gender in Healthcare","field":"Medicine","cited_by":584,"is_retracted":false,"has_abstract":false,"ca_institutions":"Canadian Institutes of Health Research; Institute of Gender and Health; Université de Montréal","funders":"National Institutes of Health; Natural Environment Research Council; Canadian Institutes of Health Research; Sight Research UK","keywords":"Gender analysis; Gender equality; Gender bias; Social analysis; Women in science; Computer science; Data science; Engineering ethics; Psychology; Political science; Sociology; Engineering; Social science; Social psychology; Gender studies","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004547183,0.0002137079,0.001030003,0.0005340427,0.00005426072,0.00003621719,0.00009612661,0.001250848,0.000007983843],"category_scores_gemma":[0.000219828,0.000149166,0.0001218322,0.001119592,0.00006033549,0.00004202574,0.0000790066,0.002074691,0.00000272379],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007347926,"about_ca_system_score_gemma":0.000370626,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004848922,"about_ca_topic_score_gemma":7.719714e-7,"domain_scores_codex":[0.9986699,0.00001546056,0.0001786404,0.0005427147,0.0003620799,0.0002312518],"domain_scores_gemma":[0.9991714,0.00009520601,0.00005999061,0.0003808231,0.0001218766,0.0001707263],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000001145501,0.00000423944,0.0001458534,0.03701763,0.0004254649,0.00001030484,0.0001413706,1.087532e-7,0.000005648944,0.00007326628,0.0001396692,0.9620353],"study_design_scores_gemma":[0.00009986157,0.00002979713,0.001395842,0.0008810147,0.003721309,0.0001245907,0.00004041917,0.0001240227,0.000001685667,0.00000651153,0.9933945,0.0001804534],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0001599494,0.998781,0.000002961842,0.00005693797,0.0002195174,0.0003933197,0.00001497999,0.00002766997,0.000343679],"genre_scores_gemma":[0.001225406,0.9975194,0.0003172354,0.0003024887,0.0001915994,0.000009196093,0.00002957267,0.00002007976,0.0003850279],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9932548,"threshold_uncertainty_score":0.9647689,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06748983419068336,"score_gpt":0.3990602283194963,"score_spread":0.331570394128813,"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."}}