{"id":"W2889499003","doi":"10.1503/cmaj.180154","title":"How predatory journals leak into PubMed","year":2018,"lang":"en","type":"article","venue":"Canadian Medical Association Journal","topic":"scientometrics and bibliometrics research","field":"Decision Sciences","cited_by":146,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ottawa Hospital","funders":"University of Ottawa","keywords":"Key (lock); Data science; Computer science; MEDLINE; World Wide Web; Computer security; Political science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":["metaresearch","bibliometrics","research_integrity"],"domain":"evaluation","study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":["metaresearch","bibliometrics","scholarly_communication","research_integrity"],"domain":"evaluation","study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"medium","status":"direct model label, unvalidated"}],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch","bibliometrics","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.06126824,0.001184826,0.001997266,0.03533729,0.005969506,0.03160297,0.003480858,0.007807186,0.05354792],"category_scores_gemma":[0.5102728,0.001704597,0.001729633,0.04236661,0.008391929,0.04335576,0.01388387,0.006791698,0.02938799],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006529638,"about_ca_system_score_gemma":0.01410522,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005257629,"about_ca_topic_score_gemma":0.007319982,"domain_scores_codex":[0.8460042,0.03480405,0.02091782,0.01027687,0.08453272,0.003464274],"domain_scores_gemma":[0.3830602,0.3138468,0.1247655,0.05031252,0.1131871,0.01482789],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0003910713,0.0000667796,0.02094973,0.00610506,0.0004685959,0.001539182,0.008554732,0.0002400728,0.000946662,0.03885384,0.7222002,0.1996841],"study_design_scores_gemma":[0.00008913928,0.0000958541,0.009234953,0.01245702,0.0004043035,0.003364495,0.007307191,0.0007504401,0.001270017,0.05724515,0.9075545,0.0002268638],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.02221789,0.06606442,0.01565723,0.782558,0.02160492,0.0006766502,0.01194214,0.004988086,0.07429064],"genre_scores_gemma":[0.271748,0.112082,0.04906026,0.4196967,0.04620954,0.0009157477,0.01748231,0.01026998,0.07253554],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9921928,"threshold_uncertainty_score":0.3240213,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3334433946008996,"score_gpt":0.4961553382486549,"score_spread":0.1627119436477553,"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."}}