{"id":"W4246529221","doi":"10.5539/cis.v11n1p108","title":"Reviewer Acknowledgements for Computer and Information Science, Vol. 11, No. 1","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; Information retrieval; Data science; Human–computer interaction","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":[0.001352322,0.0001595756,0.0001660462,0.0006036984,0.0008094325,0.003117017,0.001168607,0.00003804263,0.00001195978],"category_scores_gemma":[0.0007734692,0.0001344606,0.00002724688,0.001188069,0.001172845,0.09248853,0.001264992,0.00005353097,0.0006695562],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004896462,"about_ca_system_score_gemma":0.0002597468,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002012538,"about_ca_topic_score_gemma":6.282101e-7,"domain_scores_codex":[0.998365,0.000008489709,0.0004825168,0.0003135494,0.000424624,0.0004058016],"domain_scores_gemma":[0.988886,0.00004300757,0.000200595,0.0004583895,0.01016068,0.0002513226],"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":[0.000004787119,0.00001882683,0.0004886041,0.0001194711,0.000003723683,5.021376e-8,0.001351024,0.00000970303,0.000007933398,0.04912939,0.1058864,0.8429801],"study_design_scores_gemma":[0.0003417258,0.0001557634,0.005220322,0.00003913915,0.000001703201,0.000003160931,0.000006667145,0.3675974,0.0001526979,0.0002276597,0.6260902,0.000163543],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004276003,0.0000259966,0.976778,0.0001208494,0.009123573,0.0004805363,0.00001585558,0.00009531608,0.009083826],"genre_scores_gemma":[0.1384093,0.0004177045,0.8232192,0.03249726,0.0049616,0.0001389307,0.0001176937,0.00001421248,0.0002240478],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8428165,"threshold_uncertainty_score":0.9979178,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02203078185172409,"score_gpt":0.2627610459177459,"score_spread":0.2407302640660218,"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."}}