{"id":"W4253142094","doi":"10.5539/cis.v12n2p155","title":"Reviewer Acknowledgements for Computer and Information Science, Vol. 12, No. 2","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; 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":[0.03527848,0.002939128,0.006204672,0.01177548,0.005042416,0.009948519,0.005094789,0.01498363,0.1393222],"category_scores_gemma":[0.3717205,0.00175734,0.003827015,0.004852271,0.002434336,0.006519543,0.003605314,0.008971741,0.08221152],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004865487,"about_ca_system_score_gemma":0.009014557,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003109187,"about_ca_topic_score_gemma":0.004937976,"domain_scores_codex":[0.9571341,0.007477777,0.008227596,0.003259535,0.02216695,0.001733953],"domain_scores_gemma":[0.2424681,0.02726139,0.01144673,0.007077235,0.7023179,0.009428608],"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":[0.00002655964,0.000004089407,0.00007259315,0.0003474086,0.000007234452,0.00005890238,0.00002559177,0.000008349625,0.00003968985,0.0001087139,0.9949381,0.004362646],"study_design_scores_gemma":[0.0001606755,0.00004310244,0.0008606237,0.002226669,0.00007389125,0.0009455266,0.0002498364,0.0002706903,0.0002771927,0.001150485,0.9936351,0.0001062639],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"other","genre_scores_codex":[0.0001441797,0.003532871,0.001184273,0.1081548,0.8801656,0.0005953342,0.0007307814,0.0004506801,0.005041538],"genre_scores_gemma":[0.00513496,0.008835006,0.003148967,0.1409199,0.7342302,0.002786943,0.001970012,0.001359347,0.1016147],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.1393222,"threshold_uncertainty_score":0.4660791,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01695451060752471,"score_gpt":0.2453093130286776,"score_spread":0.2283548024211529,"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."}}