{"id":"W4255489682","doi":"10.5539/cis.v12n3p117","title":"Reviewer Acknowledgements for Computer and Information Science, Vol. 12, No. 3","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; 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","insufficient_payload"],"consensus_categories":["scholarly_communication"],"category_scores_codex":[0.001274616,0.0001602063,0.0001860206,0.000583857,0.0004308903,0.002892974,0.001134405,0.00003878042,0.00001541618],"category_scores_gemma":[0.0004669874,0.0001353053,0.00003132859,0.0008587777,0.0004471438,0.09838316,0.00120078,0.00007184579,0.001191038],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005218864,"about_ca_system_score_gemma":0.0002504746,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001434211,"about_ca_topic_score_gemma":1.86625e-7,"domain_scores_codex":[0.9983624,0.000008035177,0.0004760776,0.0003190124,0.0004468843,0.0003876578],"domain_scores_gemma":[0.9933321,0.00005115613,0.0002042034,0.0004865808,0.005702667,0.0002232561],"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":[0.000006224291,0.0000208638,0.00123159,0.0002439414,0.000005031759,5.057976e-8,0.001087706,0.00007637643,0.00001094181,0.04796546,0.04937601,0.8999758],"study_design_scores_gemma":[0.0004592319,0.0001346785,0.008911249,0.00004690684,0.000001500705,0.000002689157,0.00000769913,0.4379995,0.00006333602,0.0001754255,0.5520219,0.0001759062],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008491024,0.00004491039,0.9595719,0.000108496,0.01161142,0.0008752298,0.00001885809,0.0001061678,0.01917199],"genre_scores_gemma":[0.2055176,0.0008937221,0.7492639,0.04045505,0.002725395,0.0001944292,0.0002286985,0.00002162412,0.0006995344],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8997999,"threshold_uncertainty_score":0.9995866,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01726281663400575,"score_gpt":0.2454613494963215,"score_spread":0.2281985328623157,"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."}}