{"id":"W4288066478","doi":"10.5539/cis.v15n3p72","title":"Reviewer Acknowledgements for Computer and Information Science, Vol. 15, No. 3","year":2022,"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; Library science; Data 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03048219,0.002385559,0.005170583,0.009393327,0.005254702,0.0104916,0.005026768,0.01533708,0.1680282],"category_scores_gemma":[0.3101351,0.001436016,0.003772223,0.004375179,0.002215487,0.00547177,0.003221438,0.008376452,0.1106692],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005065347,"about_ca_system_score_gemma":0.01027085,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003749225,"about_ca_topic_score_gemma":0.006239843,"domain_scores_codex":[0.9638917,0.005657366,0.006278323,0.002533596,0.02004491,0.00159414],"domain_scores_gemma":[0.2956657,0.02231187,0.00943836,0.006253806,0.6558013,0.010529],"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":[0.0000191143,0.000003215741,0.00005689924,0.0001927761,0.000004793172,0.00004007453,0.00001404003,0.000007108408,0.00002626904,0.0000938578,0.9957924,0.003749508],"study_design_scores_gemma":[0.0001267583,0.00003477731,0.0006737817,0.001470609,0.00005209671,0.0006308173,0.0001603089,0.0002219887,0.000208765,0.001042025,0.9952989,0.00007913803],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"other","genre_scores_codex":[0.0001476142,0.002990787,0.001288291,0.1216492,0.8636696,0.0006842827,0.0008680712,0.0005877306,0.00811443],"genre_scores_gemma":[0.005370548,0.009323314,0.003340618,0.1637255,0.6568081,0.002853524,0.002584406,0.001634881,0.154359],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.1680282,"threshold_uncertainty_score":0.5621103,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01981926988789403,"score_gpt":0.2497164033319204,"score_spread":0.2298971334440264,"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."}}