{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00103446,0.0001640399,0.0002976175,0.0002228591,0.0008177791,0.0005165548,0.002501806,0.00001825698,0.000008054127],"category_scores_gemma":[0.00005745615,0.0001518961,0.00006924183,0.0008536038,0.000007959302,0.0003734722,0.001229988,0.000420668,0.0000505255],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001082625,"about_ca_system_score_gemma":0.00003737285,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001255899,"about_ca_topic_score_gemma":0.000003407343,"domain_scores_codex":[0.9978163,0.0005096865,0.0003315996,0.0004427379,0.0005760659,0.0003236016],"domain_scores_gemma":[0.9989117,0.0001941975,0.0001973118,0.0005704736,0.00006254776,0.00006374274],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001350689,0.009962629,0.4522478,0.025883,0.001468402,0.004064389,0.1518719,0.2620134,0.0009978912,0.003733283,0.04176908,0.04585316],"study_design_scores_gemma":[0.00193013,0.001142386,0.01324538,0.0009428076,0.00004797501,0.0001002669,0.001280468,0.9749523,0.000354647,0.0006567364,0.004233192,0.001113719],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9330379,0.0001237029,0.04738646,0.0001978176,0.01673301,0.0008655857,2.337387e-7,0.0004824144,0.001172843],"genre_scores_gemma":[0.9984946,4.860947e-7,0.0001138806,0.0002840003,0.0005063121,0.00009965504,0.00000109233,0.00001546399,0.0004845738],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7129389,"threshold_uncertainty_score":0.6289778,"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."}}