{"id":"W4234053697","doi":"10.5539/cis.v12n1p112","title":"Reviewer Acknowledgements for Computer and Information Science, Vol. 12, No. 1","year":2019,"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; 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","insufficient_payload"],"consensus_categories":["scholarly_communication"],"category_scores_codex":[0.001275083,0.0001602246,0.0001860052,0.0005840147,0.0004309202,0.002888187,0.001134982,0.00003878309,0.00001573018],"category_scores_gemma":[0.0004670889,0.0001353332,0.0000313302,0.0008590354,0.000447118,0.09832796,0.001201482,0.00007185767,0.001180614],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005202169,"about_ca_system_score_gemma":0.0002503626,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000142117,"about_ca_topic_score_gemma":1.85605e-7,"domain_scores_codex":[0.9983624,0.00000804143,0.0004759055,0.0003191245,0.00044681,0.0003876813],"domain_scores_gemma":[0.993371,0.0000515282,0.000204188,0.0004871974,0.005662808,0.0002232831],"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":[0.000006230032,0.00002099259,0.001237353,0.0002420161,0.000005064262,5.038115e-8,0.001093952,0.00007679844,0.00001098286,0.04848417,0.05136712,0.8974553],"study_design_scores_gemma":[0.0004558381,0.0001353324,0.008987836,0.00004716219,0.000001517332,0.000002719718,0.000007757554,0.439294,0.00006247635,0.0001756623,0.5506532,0.0001765444],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008455715,0.00004479977,0.9597304,0.0001090985,0.01154289,0.0008767495,0.00001913245,0.0001064606,0.0191147],"genre_scores_gemma":[0.2027362,0.0008791706,0.7522813,0.04028226,0.002687042,0.0001964468,0.000230773,0.00002151953,0.0006852708],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8972787,"threshold_uncertainty_score":0.9995971,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01760370313782659,"score_gpt":0.2458662910290525,"score_spread":0.2282625878912259,"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."}}