{"id":"W4402500857","doi":"10.48550/arxiv.2408.08442","title":"A semi-centralized multi-agent RL framework for efficient irrigation scheduling","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Multi-Agent Systems and Negotiation","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Scheduling (production processes); Distributed computing; Mathematical optimization; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0009965707,0.0005460758,0.0008356224,0.0002291508,0.000422831,0.0008501034,0.001641323,0.0007218723,0.001761561],"category_scores_gemma":[0.001421032,0.0003905573,0.000474583,0.0003039919,0.0007472681,0.0007611818,0.0009548535,0.001066677,0.0003632667],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007651413,"about_ca_system_score_gemma":0.001804622,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006793412,"about_ca_topic_score_gemma":0.006176753,"domain_scores_codex":[0.9995444,0.0001643462,0.00002001924,0.00009618318,0.0001153768,0.00005964813],"domain_scores_gemma":[0.999428,0.0002540002,0.00007943717,0.00005634969,0.0001284462,0.00005369188],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002207286,0.00002016584,0.000134538,0.0000265455,0.00001235095,0.0000311568,0.0000270298,0.9810636,0.001006133,0.004843679,0.0004230612,0.01238968],"study_design_scores_gemma":[0.000004332049,0.000007172211,0.00001757025,9.913185e-7,0.000001357046,0.00000319284,0.000002294837,0.9987509,0.00008848753,0.0009424719,0.0001796976,0.000001487159],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007712957,0.0001568509,0.9894335,0.0001372186,0.00002915875,0.00002496069,0.0000236894,0.0003233154,0.002158323],"genre_scores_gemma":[0.8595417,0.0001434381,0.1372857,0.0001015965,0.00004549414,0.000111417,0.00006885355,0.00006490808,0.002636904],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006793412,"threshold_uncertainty_score":0.01350772,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1064735067909303,"score_gpt":0.2378339255967584,"score_spread":0.131360418805828,"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."}}