{"id":"W4234354495","doi":"10.5539/cis.v12n4p123","title":"Reviewer Acknowledgements for Computer and Information Science, Vol. 12, No. 4","year":2019,"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; Data science; Information retrieval","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","insufficient_payload"],"consensus_categories":["scholarly_communication"],"category_scores_codex":[0.001275445,0.0001602153,0.0001860562,0.0005840771,0.0004310641,0.002894219,0.001135081,0.00003878308,0.0000152066],"category_scores_gemma":[0.0004616884,0.0001353261,0.00003133055,0.0008586887,0.0004472089,0.09839367,0.001201151,0.00007184398,0.001171303],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005202573,"about_ca_system_score_gemma":0.0002497132,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001423875,"about_ca_topic_score_gemma":1.872457e-7,"domain_scores_codex":[0.9983622,0.000008034322,0.0004762157,0.0003190382,0.0004468614,0.0003876933],"domain_scores_gemma":[0.993417,0.00005123862,0.0002042416,0.0004868137,0.005617386,0.0002232767],"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":[0.000006274167,0.00002086377,0.001233314,0.0002422908,0.000005029765,5.070155e-8,0.001088149,0.00007548044,0.00001080119,0.04933441,0.0489507,0.8990327],"study_design_scores_gemma":[0.0004607792,0.0001374384,0.009004527,0.00004730792,0.000001519826,0.000002729678,0.000007769057,0.4415505,0.00006268956,0.0001780359,0.5483696,0.0001770988],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008446006,0.00004497322,0.9597895,0.0001081661,0.01150988,0.000874471,0.00001868745,0.0001061179,0.01910219],"genre_scores_gemma":[0.2086733,0.0008895018,0.7463279,0.04029939,0.002690472,0.000193523,0.0002277255,0.0000215177,0.0006766494],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8988556,"threshold_uncertainty_score":0.9996064,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01740724556296689,"score_gpt":0.2458715093260979,"score_spread":0.228464263763131,"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."}}