{"id":"W4236570005","doi":"10.5539/cis.v11n3p123","title":"Reviewer Acknowledgements for Computer and Information Science, Vol. 11, No. 3","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; 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"],"consensus_categories":["scholarly_communication"],"category_scores_codex":[0.001351827,0.0001595574,0.0001660599,0.0006035354,0.0008093762,0.003122182,0.001168013,0.00003804002,0.00001172104],"category_scores_gemma":[0.0007733012,0.0001344329,0.00002724547,0.001187713,0.001172912,0.09254079,0.001264253,0.00005352212,0.0006754712],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004912176,"about_ca_system_score_gemma":0.000259863,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002031004,"about_ca_topic_score_gemma":6.316625e-7,"domain_scores_codex":[0.9983649,0.000008483104,0.0004826912,0.0003134392,0.0004246945,0.0004057768],"domain_scores_gemma":[0.9888162,0.00004269528,0.0002006019,0.0004577907,0.01023145,0.0002512819],"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":[0.000004793766,0.0000187546,0.0004874527,0.0001207,0.000003708337,5.052826e-8,0.001346416,0.00000967202,0.000007922019,0.04871668,0.1020916,0.8471923],"study_design_scores_gemma":[0.0003445001,0.0001551142,0.00517919,0.00003895322,0.000001685661,0.000003127498,0.00000662135,0.3667829,0.0001549021,0.0002275046,0.6269424,0.0001630605],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004297852,0.00002608471,0.9766591,0.0001202923,0.009186159,0.0004801435,0.00001564255,0.00009514116,0.00911964],"genre_scores_gemma":[0.1404299,0.0004249868,0.8209458,0.032665,0.00503677,0.0001376231,0.0001167369,0.00001429395,0.0002289099],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8470293,"threshold_uncertainty_score":0.9979126,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02160252724951813,"score_gpt":0.2623329399896102,"score_spread":0.240730412740092,"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."}}