{"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":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":["scholarly_communication"],"category_scores_codex":[0.001352706,0.0001595664,0.0001660917,0.0006037629,0.0008097026,0.003123525,0.001168709,0.00003804262,0.00001156169],"category_scores_gemma":[0.0007645289,0.0001344536,0.00002724718,0.00118759,0.001173083,0.09255075,0.001264644,0.00005352077,0.0006642734],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004896843,"about_ca_system_score_gemma":0.0002590731,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002016369,"about_ca_topic_score_gemma":6.337632e-7,"domain_scores_codex":[0.9983647,0.000008482203,0.0004828312,0.0003134646,0.0004246728,0.0004058141],"domain_scores_gemma":[0.9889677,0.00004276595,0.000200648,0.0004580294,0.01007957,0.0002513159],"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":[0.000004833937,0.00001876139,0.0004883127,0.0001199268,0.000003708217,5.066836e-8,0.001347455,0.000009562032,0.00000782306,0.05012245,0.1012649,0.8466122],"study_design_scores_gemma":[0.0003451682,0.0001580673,0.005226023,0.00003923032,0.000001704706,0.000003170101,0.000006671968,0.36929,0.0001531028,0.0002305607,0.6243824,0.0001639321],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004278665,0.0000261434,0.9767767,0.0001200285,0.009113571,0.0004801351,0.00001551418,0.00009517727,0.009094063],"genre_scores_gemma":[0.1427709,0.0004235288,0.8187559,0.03258151,0.004978696,0.0001371595,0.0001163911,0.00001424206,0.0002217083],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8464482,"threshold_uncertainty_score":0.9979113,"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."}}