{"id":"W4205180481","doi":"10.3390/biology11010131","title":"A Bibliometric Analysis of Mexican Bioinformatics: A Portrait of Actors, Structure, and Dynamics","year":2022,"lang":"en","type":"article","venue":"Biology","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Global Institute for Water Security; University of Saskatchewan","funders":"Dirección General de Asuntos del Personal Académico, Universidad Nacional Autónoma de México; Consejo Nacional de Ciencia y Tecnología","keywords":"Biology; Informatics; Thematic analysis; Field (mathematics); Bibliometrics; Data science; Democratization; Work (physics); Bioinformatics; Library science; Political science; Social science; Sociology; Politics; Democracy; Computer science; Qualitative research","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.003726907,0.0003661493,0.0005705584,0.07101004,0.00128321,0.00402244,0.0004663432,0.0004170857,0.003458927],"category_scores_gemma":[0.0150964,0.000160064,0.0006096305,0.1073903,0.0006821888,0.002516422,0.001239988,0.0002800974,0.0004230925],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003080656,"about_ca_system_score_gemma":0.00241366,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01121139,"about_ca_topic_score_gemma":0.009674624,"domain_scores_codex":[0.9973868,0.0006293291,0.0003211471,0.0003045592,0.001091059,0.0002672532],"domain_scores_gemma":[0.9885197,0.005054103,0.002950621,0.0004230953,0.002764046,0.0002883858],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002921979,0.0001022214,0.6532084,0.003291723,0.0004385907,0.0007913878,0.01281183,0.002583507,0.002390369,0.03402133,0.01883626,0.2712322],"study_design_scores_gemma":[0.00001713934,0.00008363001,0.8811788,0.00106449,0.0003623411,0.0007403343,0.02048819,0.005243859,0.0008913459,0.005343701,0.08452531,0.00006089402],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8726748,0.01872175,0.005211888,0.004176192,0.000113474,0.0001976973,0.02156143,0.0003263867,0.07701635],"genre_scores_gemma":[0.9782096,0.007332829,0.004104829,0.00007294561,0.0001422294,0.0001379707,0.007648624,0.00004164287,0.002309423],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9289899,"threshold_uncertainty_score":0.0223518,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01322108189643438,"score_gpt":0.2859532285476752,"score_spread":0.2727321466512408,"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."}}