{"id":"W4409978955","doi":"10.5539/cis.v18n1p168","title":"Reviewer Acknowledgements for Computer and Information Science, Vol. 18, No. 1","year":2025,"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","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":[0.001265239,0.0001534003,0.0001768652,0.0008357812,0.0006816727,0.003212607,0.001167369,0.00003854339,0.000007822949],"category_scores_gemma":[0.0008841049,0.0001322197,0.00003035603,0.001559107,0.0006346927,0.07637233,0.001371058,0.00007087077,0.0002524701],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000607105,"about_ca_system_score_gemma":0.0003769668,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001495355,"about_ca_topic_score_gemma":2.123745e-7,"domain_scores_codex":[0.9984858,0.000008665192,0.0005037155,0.0003014971,0.0003441938,0.0003561458],"domain_scores_gemma":[0.9919444,0.00006036259,0.0001635223,0.0004418792,0.007216845,0.0001729938],"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":[0.00000352237,0.00001404429,0.0002888044,0.0001615462,0.000004112265,3.55732e-8,0.0004067698,0.00003245033,0.000003379592,0.07008251,0.1121865,0.8168163],"study_design_scores_gemma":[0.0003860522,0.00006429364,0.006424712,0.0000660549,0.00000229441,0.000001272722,0.000006781242,0.3808926,0.00008076234,0.0004746952,0.6114626,0.0001378786],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001092815,0.00006346433,0.9680901,0.0001638797,0.008217384,0.0005031993,0.00001357157,0.00008449535,0.02177112],"genre_scores_gemma":[0.09111222,0.00118644,0.8428823,0.06162279,0.001957759,0.0002793768,0.0001828559,0.00001324512,0.0007630526],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8166784,"threshold_uncertainty_score":0.9978222,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02035326639873538,"score_gpt":0.2700804940398904,"score_spread":0.249727227641155,"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."}}