{"id":"W4231969571","doi":"10.5539/cis.v10n3p79","title":"Reviewer Acknowledgements for Computer and Information Science, Vol. 10, No. 3","year":2017,"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":[0.03906937,0.00280931,0.006439268,0.01007051,0.005304003,0.01053171,0.005609127,0.01661926,0.1223309],"category_scores_gemma":[0.4068357,0.001699684,0.004586288,0.004743453,0.002927033,0.006220912,0.003690925,0.00997139,0.07260787],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005897403,"about_ca_system_score_gemma":0.01077747,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003810957,"about_ca_topic_score_gemma":0.005780781,"domain_scores_codex":[0.9524521,0.007959252,0.009657105,0.00336057,0.02457524,0.001995759],"domain_scores_gemma":[0.2182006,0.02630785,0.01221995,0.006755224,0.7271131,0.009403235],"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":[0.00002972339,0.000004245881,0.00009374387,0.0003789306,0.00000890011,0.00006873567,0.00002954562,0.00001062609,0.00003856001,0.0001312572,0.9944613,0.004744424],"study_design_scores_gemma":[0.0001946007,0.00004050305,0.000908631,0.002635119,0.00009178979,0.001061961,0.0002826217,0.0003321669,0.000300778,0.001624017,0.9924078,0.0001200786],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.0001645849,0.003380025,0.001356077,0.1222508,0.8658568,0.0007766224,0.0007470464,0.0005093841,0.00495857],"genre_scores_gemma":[0.00636628,0.009358861,0.003694231,0.1666081,0.7203695,0.003457895,0.001847832,0.001454727,0.08684256],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.1223309,"threshold_uncertainty_score":0.4092377,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02819607928593883,"score_gpt":0.2756728234950196,"score_spread":0.2474767442090808,"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."}}