{"id":"W4231969571","doi":"10.5539/cis.v10n3p79","title":"Reviewer Acknowledgements for Computer and Information Science, Vol. 10, No. 3","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; 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":["sts","scholarly_communication"],"consensus_categories":["scholarly_communication"],"category_scores_codex":[0.001422889,0.0001591313,0.00017789,0.0004460182,0.001726862,0.007719981,0.001983893,0.00003851344,0.00001281612],"category_scores_gemma":[0.00151474,0.0001346584,0.00003136421,0.00038594,0.001013165,0.1354546,0.001953288,0.00006421725,0.0006516678],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004437104,"about_ca_system_score_gemma":0.000249958,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000234388,"about_ca_topic_score_gemma":3.15664e-7,"domain_scores_codex":[0.9984457,0.000006476472,0.0004452284,0.000306216,0.0004163002,0.0003800674],"domain_scores_gemma":[0.9909738,0.00003716853,0.0003421663,0.0007979224,0.007598505,0.0002504678],"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":[0.00000411907,0.00001271518,0.0003460594,0.0001075288,0.000003305787,6.641172e-8,0.0005106684,0.00001376802,0.00000305111,0.02851708,0.0643464,0.9061352],"study_design_scores_gemma":[0.0004302322,0.0000989898,0.01462549,0.00004710825,0.000001895886,0.000002466007,0.000004161911,0.3572332,0.00006477484,0.0002541913,0.6270644,0.000173061],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002556493,0.00003757755,0.9621089,0.0002102233,0.009760213,0.0005764627,0.00002292699,0.00008425883,0.02464297],"genre_scores_gemma":[0.1704165,0.001048018,0.8000013,0.02283331,0.004257516,0.0002297532,0.0001663004,0.00002019745,0.001027135],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9059622,"threshold_uncertainty_score":0.9995728,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02819607928593883,"score_gpt":0.2756728234950196,"score_spread":0.2474767442090808,"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."}}