{"id":"W4253782455","doi":"10.5539/cis.v10n1p89","title":"Reviewer Acknowledgements for Computer and Information Science, Vol. 10, No. 1","year":2017,"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; Engineering ethics; Engineering","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":["sts","scholarly_communication"],"consensus_categories":["scholarly_communication"],"category_scores_codex":[0.001423411,0.0001591494,0.0001778753,0.0004461386,0.001726982,0.007707268,0.001984901,0.00003851609,0.00001307717],"category_scores_gemma":[0.001515068,0.0001346862,0.00003136583,0.0003860558,0.001013107,0.1353817,0.001954429,0.00006422788,0.0006459611],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004422909,"about_ca_system_score_gemma":0.0002498463,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002322568,"about_ca_topic_score_gemma":3.139387e-7,"domain_scores_codex":[0.9984458,0.000006481513,0.0004450674,0.0003063236,0.000416231,0.0003800904],"domain_scores_gemma":[0.9910254,0.00003743942,0.0003421456,0.000798945,0.007545608,0.0002505018],"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":[0.000004115051,0.0000127694,0.0003470195,0.0001064778,0.000003320832,6.602549e-8,0.0005126267,0.00001381784,0.000003056749,0.02877106,0.0667771,0.9034486],"study_design_scores_gemma":[0.000426927,0.00009944118,0.01474639,0.00004735078,0.000001916328,0.000002493298,0.000004192261,0.3581463,0.00006387686,0.0002544596,0.626033,0.0001736379],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002545536,0.0000374811,0.9622517,0.0002113686,0.009701519,0.0005774036,0.00002325813,0.00008448241,0.02456727],"genre_scores_gemma":[0.1679889,0.001030239,0.8026409,0.02272004,0.004194694,0.0002319763,0.0001676925,0.00002008581,0.001005494],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.903275,"threshold_uncertainty_score":0.9995726,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02875460058313218,"score_gpt":0.2761192819825631,"score_spread":0.247364681399431,"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."}}