{"id":"W1565999497","doi":"10.16995/dscn.37","title":"Digital Humanities at Siberian Federal University","year":2015,"lang":"en","type":"article","venue":"Digital Studies / Le champ numérique","topic":"Library Science and Information","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Digital humanities; Scholarship; Digital scholarship; Humanities; Promotion (chess); Library science; Incentive; Political science; Computer science; Art; Politics; Law","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003562907,0.0003269628,0.0005561032,0.002198056,0.005851553,0.005559041,0.0006118783,0.001209229,0.06167429],"category_scores_gemma":[0.006728038,0.0001988924,0.0003112063,0.005062837,0.00204755,0.002539425,0.003852475,0.00105472,0.009870601],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005786344,"about_ca_system_score_gemma":0.01112712,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0236096,"about_ca_topic_score_gemma":0.01484102,"domain_scores_codex":[0.9984861,0.0005658168,0.0001125441,0.0002018658,0.0003951921,0.0002384431],"domain_scores_gemma":[0.9973232,0.0006437065,0.0002200142,0.0005020985,0.0007735853,0.0005373788],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004077822,0.000144718,0.04167451,0.001476555,0.00006340175,0.001247022,0.01442314,0.001286069,0.001592489,0.1918086,0.2249093,0.5209664],"study_design_scores_gemma":[0.00002713832,0.00004471265,0.07240944,0.0007565792,0.00002441551,0.0004297524,0.006965004,0.0007757721,0.001328078,0.01513496,0.9020653,0.00003883095],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2196832,0.03163888,0.004172778,0.04235944,0.00480588,0.0001527617,0.007780872,0.001000056,0.6884061],"genre_scores_gemma":[0.652217,0.01116088,0.004336922,0.002346474,0.0005542447,0.0002183887,0.004266917,0.000180604,0.3247185],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.06167429,"threshold_uncertainty_score":0.2063211,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04277334331907143,"score_gpt":0.2147359622657168,"score_spread":0.1719626189466453,"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."}}