{"id":"W6902073806","doi":"10.6084/m9.figshare.11568639.v1","title":"A Short Talk on Digital Collaboration Topic Based on Bibliometric Data. 2015-2019","year":2020,"lang":"en","type":"article","venue":"Figshare","topic":"Library Science and Information","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"European Commission; CERN; National Science Foundation","keywords":"Digital library; Service (business); Citation; Big data; Digital transformation; Webometrics; Co-citation; Digital humanities","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.003811082,0.0010639,0.001074272,0.0668235,0.001507311,0.006699527,0.0006829987,0.001448673,0.1725142],"category_scores_gemma":[0.01665157,0.0004329814,0.0009976864,0.0726114,0.0005376395,0.008275307,0.003472093,0.0009690569,0.07709002],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001679758,"about_ca_system_score_gemma":0.002716689,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006404307,"about_ca_topic_score_gemma":0.008538997,"domain_scores_codex":[0.9981526,0.0002701254,0.0004030464,0.0002352488,0.0006792609,0.0002597114],"domain_scores_gemma":[0.9910975,0.003147726,0.00145689,0.0003266885,0.002717653,0.001253643],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.00006165191,0.00001063761,0.002987793,0.002010687,0.00002720237,0.00007075224,0.0002211593,0.0000744128,0.0001989438,0.002496242,0.9213659,0.07047467],"study_design_scores_gemma":[0.00001216126,0.00002305268,0.01597825,0.001790381,0.0000342386,0.000199069,0.0007468274,0.0001258707,0.0001824138,0.002090083,0.9787719,0.00004576264],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.007823905,0.06794015,0.005741299,0.04799217,0.02443282,0.0004663195,0.6829975,0.005984439,0.1566214],"genre_scores_gemma":[0.07054519,0.07056002,0.0107094,0.01052731,0.02821351,0.001129313,0.6429331,0.003349542,0.1620326],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9331765,"threshold_uncertainty_score":0.5771174,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0778892735245849,"score_gpt":0.2812869941376818,"score_spread":0.2033977206130969,"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."}}