{"id":"W4391984740","doi":"10.26512/rici.v15.n2.2022.40762","title":"Perspectivas arquivísticas na Gestão de Dados de Pesquisa","year":2022,"lang":"en","type":"article","venue":"Revista Ibero-Americana de Ciência da Informação","topic":"Research Data Management Practices","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Multidisciplinary approach; Library science; Humanities; Research data; Archival science; Sociology; Political science; Philosophy; Computer science; Social science; Subject (documents)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.003746672,0.0004070718,0.0004800564,0.0005603633,0.0009190667,0.003465176,0.005717766,0.00006521193,0.0004084754],"category_scores_gemma":[0.001910436,0.0004462397,0.0002126303,0.00235303,0.0002431997,0.01050823,0.00404398,0.001166772,0.0001474403],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002501123,"about_ca_system_score_gemma":0.001427587,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001956483,"about_ca_topic_score_gemma":0.00001038423,"domain_scores_codex":[0.9946173,0.0006722755,0.0006540517,0.0008069498,0.001490274,0.001759169],"domain_scores_gemma":[0.995559,0.000629984,0.0005486109,0.00240273,0.0001580324,0.0007016009],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001536174,0.0006025702,0.02052903,0.0004520201,0.0005418618,0.001528278,0.009500736,0.003032954,0.002116629,0.7961607,0.02716191,0.1382197],"study_design_scores_gemma":[0.001044212,0.0007990798,0.01174663,0.00006449334,0.0001203899,0.0008941035,0.005437255,0.1062339,0.0002306848,0.0009744826,0.8710981,0.001356701],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06259464,0.0009427716,0.8955338,0.006132658,0.0002373016,0.001263582,0.0001353565,0.0007918638,0.032368],"genre_scores_gemma":[0.9141783,0.0009205443,0.07469334,0.007156959,0.0001741569,0.0003601875,0.0000890146,0.00008022106,0.002347286],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8515837,"threshold_uncertainty_score":0.999799,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04452405129925524,"score_gpt":0.3258109037191082,"score_spread":0.281286852419853,"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."}}