{"id":"W4230605178","doi":"10.5539/cis.v13n1p99","title":"Reviewer Acknowledgements for Computer and Information Science, Vol. 13, No. 1","year":2020,"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; Library 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":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":["scholarly_communication"],"category_scores_codex":[0.0008615348,0.0001587876,0.000181605,0.0003567661,0.0005348428,0.002949525,0.00116122,0.00003421344,0.000008622155],"category_scores_gemma":[0.0009949732,0.0001365959,0.00003079047,0.001225418,0.0007117523,0.08812962,0.001300176,0.00007605547,0.0005545432],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003502599,"about_ca_system_score_gemma":0.0002452692,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001463537,"about_ca_topic_score_gemma":1.653684e-7,"domain_scores_codex":[0.9984043,0.000008277173,0.0004877061,0.0003202413,0.0004253104,0.0003542302],"domain_scores_gemma":[0.9931917,0.00004234083,0.0001921132,0.0003212678,0.005889842,0.0003627381],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000006525192,0.00001387643,0.0003115321,0.0002154049,0.000004452122,9.246024e-8,0.002008349,0.00008117975,0.000006794849,0.0308893,0.1051337,0.8613288],"study_design_scores_gemma":[0.0003466504,0.0001281211,0.00259534,0.00002510807,0.000001650311,0.000001598892,0.000009866215,0.5127209,0.00006417944,0.00009667736,0.4838635,0.0001464517],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001577707,0.00005046504,0.9871231,0.0005154997,0.004359526,0.0005013962,0.00002012247,0.0001065813,0.005745572],"genre_scores_gemma":[0.143364,0.0009503543,0.7458413,0.1055072,0.003885569,0.0001591641,0.000189667,0.00001754709,0.00008507535],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8611824,"threshold_uncertainty_score":0.9980855,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02734111285944002,"score_gpt":0.2522371246577883,"score_spread":0.2248960117983483,"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."}}