{"id":"W3045459043","doi":"10.25281/0869-608x-2020-69-2-135-146","title":"Global Trends in Marketing Technologies to Promote Library Websites","year":2020,"lang":"en","type":"article","venue":"Bibliotekovedenie [Russian Journal of Library Science]","topic":"Web and Library Services","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Promotion (chess); The Internet; Social media; Digital marketing; Business; World Wide Web; Influencer marketing; Digital library; Marketing; Political science; Computer science; Marketing management; Relationship marketing","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":[],"consensus_categories":[],"category_scores_codex":[0.001687128,0.0004423909,0.0003062492,0.0047903,0.000823075,0.005879839,0.0005516104,0.001174724,0.01496366],"category_scores_gemma":[0.00438621,0.0002073806,0.0004167193,0.008453889,0.0008649997,0.004906523,0.001328826,0.001561078,0.00663755],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001832703,"about_ca_system_score_gemma":0.001390171,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002306596,"about_ca_topic_score_gemma":0.002718421,"domain_scores_codex":[0.9982623,0.0003061772,0.0001008772,0.0003244156,0.0008183653,0.0001877779],"domain_scores_gemma":[0.9933345,0.001867877,0.001349315,0.0003413166,0.002385973,0.0007209352],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001602807,0.0003541532,0.05906844,0.002749894,0.00007128085,0.0004331283,0.006628291,0.0004234125,0.004856577,0.05521593,0.06772521,0.8023133],"study_design_scores_gemma":[0.00001216945,0.0001143191,0.167334,0.0008390758,0.00006082977,0.001171305,0.006612903,0.0006587904,0.002017085,0.003510359,0.817618,0.00005124471],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3330612,0.1049548,0.007125024,0.03841883,0.00155398,0.0003102317,0.003666688,0.001224323,0.5096848],"genre_scores_gemma":[0.7535899,0.1014786,0.01139451,0.01148464,0.002461389,0.0002671262,0.003688414,0.0005179354,0.1151175],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01496366,"threshold_uncertainty_score":0.05005842,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01388926662478225,"score_gpt":0.2379726850792131,"score_spread":0.2240834184544309,"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."}}