{"id":"W4241205120","doi":"10.5539/cis.v11n4p84","title":"Reviewer Acknowledgements for Computer and Information Science, Vol. 11, No. 4","year":2018,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Big Data and Digital Economy","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Library science; Information retrieval; Data science","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":[0.03766491,0.002730482,0.006131005,0.01067968,0.005356654,0.009663542,0.005666627,0.01516218,0.1317452],"category_scores_gemma":[0.4111997,0.001688725,0.004184703,0.004527512,0.002810676,0.006575789,0.003813032,0.009250245,0.07213037],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00578692,"about_ca_system_score_gemma":0.01030954,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003668357,"about_ca_topic_score_gemma":0.005502593,"domain_scores_codex":[0.9559616,0.007604288,0.008979322,0.003073278,0.02250068,0.001880878],"domain_scores_gemma":[0.2147821,0.02649021,0.01117285,0.006763975,0.7318619,0.008929044],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002976196,0.000004171146,0.00009678584,0.0003905852,0.000009088066,0.00006988829,0.00003072053,0.0000111222,0.00003956477,0.0001453202,0.9941671,0.005005812],"study_design_scores_gemma":[0.0001752199,0.00004185425,0.0008773328,0.002305041,0.00008985781,0.0009367922,0.0002953343,0.0002929276,0.0002904103,0.0015748,0.9930062,0.0001141714],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"other","genre_scores_codex":[0.0001636895,0.003132504,0.001231989,0.1182195,0.8710421,0.0006239336,0.0006652867,0.0004122526,0.004508635],"genre_scores_gemma":[0.006283201,0.008968682,0.003613736,0.1417165,0.7274749,0.003140223,0.001832436,0.001260601,0.1057097],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.1317452,"threshold_uncertainty_score":0.4407317,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02178345674666355,"score_gpt":0.2627638200137688,"score_spread":0.2409803632671053,"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."}}