{"id":"W4295308516","doi":"10.1109/access.2022.3206366","title":"Serverless on Machine Learning: A Systematic Mapping Study","year":2022,"lang":"en","type":"article","venue":"IEEE Access","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Computer science; Workflow; Machine learning; Software deployment; Pipeline (software); Artificial intelligence; Cloud computing; Pipeline transport; Software engineering; Database; Operating system","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01314286,0.0006087886,0.0008312973,0.01436133,0.001480901,0.002552739,0.001068003,0.0007538451,0.002448624],"category_scores_gemma":[0.05176799,0.0005722222,0.001222844,0.0174288,0.00140502,0.005221171,0.001722868,0.001141902,0.0004844031],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00371089,"about_ca_system_score_gemma":0.01136063,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006900805,"about_ca_topic_score_gemma":0.01171639,"domain_scores_codex":[0.985311,0.007824785,0.00150935,0.001275765,0.003575483,0.000503654],"domain_scores_gemma":[0.8848459,0.07673389,0.008407176,0.006338443,0.02261455,0.001059966],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"systematic_review","study_design_scores_codex":[0.0001874578,0.0005750915,0.09467832,0.03078565,0.0006998233,0.0006946432,0.01495303,0.002403799,0.002949971,0.0375217,0.00589874,0.8086517],"study_design_scores_gemma":[0.0001419396,0.004900758,0.2889559,0.1008428,0.005467593,0.004747723,0.09837613,0.02399632,0.02737451,0.06403597,0.3808312,0.0003291568],"study_design_candidate":"systematic_review","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"review","genre_scores_codex":[0.5130413,0.3256331,0.1001413,0.005106747,0.0003936316,0.003732878,0.002185605,0.0005619134,0.04920357],"genre_scores_gemma":[0.7632554,0.145912,0.08324283,0.00139342,0.0001178323,0.001441796,0.001442632,0.0001274222,0.003066621],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.01436133,"threshold_uncertainty_score":0.06950688,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06015835948489747,"score_gpt":0.2931308232736781,"score_spread":0.2329724637887806,"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."}}