{"id":"W4234053697","doi":"10.5539/cis.v12n1p112","title":"Reviewer Acknowledgements for Computer and Information Science, Vol. 12, No. 1","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","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":[0.04095726,0.002683331,0.006376011,0.01265907,0.005562216,0.009794548,0.005411592,0.01500245,0.1340117],"category_scores_gemma":[0.4309902,0.00173843,0.003844583,0.005365996,0.002564548,0.006583853,0.003740872,0.009733985,0.0800956],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005794774,"about_ca_system_score_gemma":0.01046731,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00354737,"about_ca_topic_score_gemma":0.005857101,"domain_scores_codex":[0.9525279,0.008718126,0.009484008,0.003746479,0.02366244,0.00186092],"domain_scores_gemma":[0.2076404,0.03184463,0.01278103,0.00718785,0.7315816,0.008964499],"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":[0.00002392169,0.000003948884,0.00007805705,0.0003897799,0.000006822331,0.00004743175,0.00002992379,0.000008587311,0.00003309433,0.0001165567,0.9946549,0.004607137],"study_design_scores_gemma":[0.0001330488,0.00003912001,0.0008607921,0.0026173,0.00007401279,0.0007682063,0.0002637021,0.0002266202,0.0002358053,0.001237882,0.9934373,0.0001060298],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.0001413042,0.004021496,0.001139442,0.1345699,0.8536186,0.0006075561,0.0008409306,0.0004410628,0.004619723],"genre_scores_gemma":[0.005257437,0.01076156,0.003485351,0.1607508,0.7221406,0.003123824,0.002295085,0.001399206,0.0907862],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.1340117,"threshold_uncertainty_score":0.4483137,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01760370313782659,"score_gpt":0.2458662910290525,"score_spread":0.2282625878912259,"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."}}