{"id":"W6949912290","doi":"10.5281/zenodo.4306413","title":"Technical Appendix to Economic Methodology: A Bibliometric Perspective","year":2020,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Sex and Gender in Healthcare","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Université de Sherbrooke","funders":"","keywords":"Perspective (graphical); Bibliometrics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":["bibliometrics"],"domain":null,"study_design":"not_applicable","genre":"other","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":["bibliometrics"],"domain":null,"study_design":"design_other","genre":"other","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"}],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch","bibliometrics","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.006595665,0.0009607995,0.001124075,0.01516107,0.0009395956,0.004262262,0.001578345,0.00217224,0.4371727],"category_scores_gemma":[0.09876372,0.0007578134,0.0007608286,0.03060254,0.0006458619,0.003050608,0.001688402,0.001651309,0.1743518],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002380966,"about_ca_system_score_gemma":0.005320575,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004432376,"about_ca_topic_score_gemma":0.003818704,"domain_scores_codex":[0.9935815,0.002516949,0.0008907021,0.0003407493,0.002518656,0.0001513462],"domain_scores_gemma":[0.9192361,0.05899969,0.003338335,0.002319017,0.01536622,0.0007408381],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001211604,0.00002616149,0.0002005694,0.0007944158,0.00001180581,0.00003943157,0.00003072598,0.0004169052,0.000035384,0.02788839,0.9427425,0.02780169],"study_design_scores_gemma":[0.0000346868,0.00001806278,0.0009997112,0.000903228,0.00001928859,0.0001365984,0.0001112722,0.000893274,0.0001690915,0.04479339,0.9518948,0.00002657658],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"methods","genre_scores_codex":[0.001433003,0.005504375,0.1126509,0.03358459,0.008043754,0.002343674,0.5693648,0.004374235,0.2627006],"genre_scores_gemma":[0.02864894,0.01669446,0.2271429,0.009367417,0.008722304,0.009241258,0.440718,0.005173741,0.254291],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9934043,"threshold_uncertainty_score":0.8028048,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1728762878124956,"score_gpt":0.3830131059718255,"score_spread":0.2101368181593299,"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."}}