{"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":"codex-gemma-dda1882f352a","candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.0003857665,0.0001205051,0.0003540306,0.01308994,0.00005877389,0.000006266894,0.0003085236,0.0001370724,0.0001377739],"category_scores_gemma":[0.000238614,0.0001017858,0.0001355744,0.01780983,0.000372999,0.000002824885,0.0004619593,0.0001104051,2.942671e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001742069,"about_ca_system_score_gemma":0.0001230731,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009795103,"about_ca_topic_score_gemma":0.00007736506,"domain_scores_codex":[0.9987519,0.00007072557,0.0005140621,0.0001760151,0.0002337603,0.0002534934],"domain_scores_gemma":[0.9991375,0.00004558516,0.0002778674,0.000302574,0.0001272711,0.0001092098],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004090805,0.0003790858,0.4696045,0.0004447305,0.004394862,0.000002634666,0.0005613856,0.0001887167,0.2941364,0.002300662,0.001363786,0.2262141],"study_design_scores_gemma":[0.003130861,0.009814073,0.7917922,0.00001574625,0.001474127,0.00006201123,0.003211697,0.05195319,0.09112207,0.001441779,0.04480899,0.001173305],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9959038,0.0006337324,0.001341977,0.0000499083,0.00008481488,0.0001336492,0.001434339,0.000004216733,0.0004134919],"genre_scores_gemma":[0.9962206,0.0005058263,0.00177106,0.00007747166,0.00002184265,0.000006360687,0.001333905,0.000006783006,0.00005610054],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3221876,"threshold_uncertainty_score":0.9980959,"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."}}