{"id":"W4396514371","doi":"10.5539/cis.v17n1p57","title":"Reviewer Acknowledgements for Computer and Information Science, Vol. 17, No. 1","year":2024,"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":[0.001369924,0.0001602873,0.0001557091,0.0007614681,0.0005115978,0.00589157,0.0009673076,0.00003707423,0.0000113383],"category_scores_gemma":[0.0004854532,0.0001317547,0.0000345614,0.001405532,0.0005785497,0.106411,0.001062805,0.00008278705,0.0006326148],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005971593,"about_ca_system_score_gemma":0.0003301473,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000112914,"about_ca_topic_score_gemma":1.682548e-7,"domain_scores_codex":[0.9983994,0.000007009173,0.0004699401,0.0003350082,0.0004217167,0.0003669376],"domain_scores_gemma":[0.9944814,0.00006368054,0.0001020281,0.000370816,0.004756612,0.0002254749],"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":[0.000001777296,0.000007841307,0.00004934425,0.0002124158,0.000003839621,1.177466e-7,0.0008125059,0.00003036316,0.000003412528,0.0515369,0.1248409,0.8225006],"study_design_scores_gemma":[0.0001399538,0.00006787804,0.001203729,0.00006867408,0.000001770435,0.000003771332,0.00000437846,0.4564318,0.00003468768,0.0002629026,0.5416597,0.0001207405],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0009091878,0.0001719097,0.9761425,0.0001760651,0.01085447,0.0004608636,0.00002393257,0.0001641819,0.01109688],"genre_scores_gemma":[0.09612645,0.002060518,0.8572712,0.03769255,0.005383161,0.0003146719,0.0002941322,0.00002752012,0.0008298231],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8223798,"threshold_uncertainty_score":0.9951404,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02056657501348732,"score_gpt":0.2637836315254728,"score_spread":0.2432170565119855,"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."}}