{"id":"W4247081944","doi":"10.5539/cis.v8n3p292","title":"Reviewer Acknowledgements for Computer and Information Science, Vol. 8, No. 3","year":2015,"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.03718608,0.002775563,0.006214601,0.0101933,0.005212522,0.01015184,0.005558113,0.01659105,0.1236716],"category_scores_gemma":[0.3935486,0.00170355,0.00458043,0.004623767,0.002906106,0.006026363,0.003590609,0.009984211,0.07311475],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005655764,"about_ca_system_score_gemma":0.01005536,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003568291,"about_ca_topic_score_gemma":0.005486656,"domain_scores_codex":[0.9553863,0.007423466,0.008986593,0.003191729,0.02312526,0.001886681],"domain_scores_gemma":[0.2286445,0.02649589,0.01205049,0.006930829,0.7164992,0.009379012],"domain_codex":null,"domain_gemma":"incentives","domain_candidate":"incentives","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002923354,0.000004150197,0.00008740349,0.0003608478,0.000008526484,0.00007073015,0.00002934082,0.00001058678,0.00004141947,0.0001289641,0.9944386,0.004790088],"study_design_scores_gemma":[0.0001983033,0.00004122562,0.0008952426,0.00253123,0.00008922092,0.001106835,0.0002698929,0.0003297542,0.0003107984,0.001619824,0.9924907,0.0001169948],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"other","genre_scores_codex":[0.0001622007,0.003271335,0.001372364,0.1227538,0.8656961,0.0007380373,0.0006688709,0.0005136617,0.004823581],"genre_scores_gemma":[0.005865194,0.008504344,0.003590204,0.1641714,0.7267024,0.003229461,0.001657132,0.001422423,0.08485734],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.9628139,"threshold_uncertainty_score":0.4137227,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03410358676360445,"score_gpt":0.2678298939607005,"score_spread":0.2337263071970961,"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."}}