{"id":"W7114893741","doi":"10.5281/zenodo.17898820","title":"Cloud-Native Scheduling and Resource Orchestration: A Deep Dive into AI-Driven Approaches","year":2025,"lang":"","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nutrasource","funders":"European Commission","keywords":"Cloud computing; Scheduling (production processes); Workload; Resource (disambiguation); Key (lock); Edge computing","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":[0.002414336,0.000938817,0.0008216253,0.0009846536,0.0005840395,0.003727004,0.002055402,0.001005022,0.001717438],"category_scores_gemma":[0.003679304,0.0004901273,0.0006662846,0.001271851,0.002110285,0.00356835,0.001440237,0.002698162,0.0005554529],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001935429,"about_ca_system_score_gemma":0.002773025,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004343099,"about_ca_topic_score_gemma":0.00424839,"domain_scores_codex":[0.9987357,0.0004282897,0.00009049417,0.0002257988,0.0004069188,0.0001127151],"domain_scores_gemma":[0.9965242,0.002267869,0.0002263189,0.0003787807,0.0004522459,0.0001506261],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001321939,0.0002905924,0.001921452,0.002125199,0.0002409795,0.0001191288,0.0005087162,0.3112172,0.005812062,0.3645246,0.007950123,0.3051578],"study_design_scores_gemma":[0.00002974794,0.00009412818,0.0005252968,0.0002770911,0.00004607728,0.00007704236,0.0002959869,0.6567311,0.002619969,0.2838292,0.05541281,0.00006160385],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01089943,0.0263601,0.9353437,0.006699655,0.0003332476,0.000103984,0.0001227986,0.0007534395,0.01938367],"genre_scores_gemma":[0.4482144,0.04492274,0.4946215,0.002473274,0.001112308,0.0002579079,0.0004158004,0.000529894,0.007452183],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004343099,"threshold_uncertainty_score":0.01404256,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03956323915057029,"score_gpt":0.246687721542542,"score_spread":0.2071244823919717,"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."}}